Methods and systems for image processing

ABSTRACT

Methods and systems for image processing are provided. A target image may be acquired, wherein the target image may include a plurality of elements, an element of which may correspond to a pixel or a voxel. The target image may be decomposed into at least one layer, wherein the at least one layer may include a low frequency sub-image and a high frequency sub-image. The at least one layer may be transformed. The transformed layer may be reconstructed into a composite image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.201511027401.7, filed on Dec. 31, 2015, Chinese Patent Application No.201511027173.3, filed on Dec. 31, 2015, Chinese Patent Application No.201610510790.7, filed on Jul. 1, 2016, and Chinese Patent ApplicationNo. 201610584749.4, filed on Jul. 22, 2016. Each of the above-referencedapplications is expressly incorporated herein by reference to theirentireties.

TECHNICAL FIELD

The present disclosure generally relates to image processing, morespecifically relates to methods and systems for enhancing an image.

BACKGROUND

An imaging system may play a significant role in the medical filed. Animaging system may generate and/or process a medical image (e.g., a CTimage, a PET image, an MM image, etc.) for medical diagnose orradioactive therapy. For instance, a CT image of a breast may be used toscreen a lump in the breast. Usually, a medical image may be adjusted,in order to facilitate a doctor to identify a potential lesion. Forinstance, the image may be denoised and/or enhanced by differenttechniques of image processing. However, the adjustment for the imagemay be inefficient and/or ineffective. For instance, an edge of a regionof interest may be missed; gray levels in the image may be uneven; orimaging noise may be enhanced. Hence, image processing technique(s) thatmay enhance a contrast of the image and/or denoise the image, may bedesirous for the imaging system.

SUMMARY

One aspect of the present disclosure relates to a method for imageprocessing. The method may be implemented on at least one machine, eachof which may have at least one processor and storage. The method mayinclude one or more of the following operations. A target image may beacquired, wherein the target image may include a plurality of elements,an element of which may correspond to a pixel or a voxel. The targetimage may be decomposed into at least one layer, wherein the at leastone layer may include a low frequency sub-image and a high frequencysub-image. The at least one layer may be transformed. The transformedlayer may be reconstructed into a composite image.

Another aspect of the present disclosure relates to a non-transitorycomputer readable medium including executable instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to effectuate a method for image processing.

A further aspect of the present disclosure relates to a system for imageprocessing. The system may include at least one processor andinstructions. The instructions, when executed by the at least oneprocessor, may perform a method for image processing. The system mayfurther include a non-transitory computer readable medium including theinstructions.

In some embodiments, the acquisition of a target image may include oneor more of the following operations. An initial image may be acquired. Aregion of interest (ROI) may be extracted based on the initial image. AnROI edge may be extracted based on the initial image. An ROI image maybe determined as the target image based on the ROI and the ROI edge.

In some embodiments, the ROI may be a breast. The ROI edge may be abreast edge. The target image may be a breast image.

In some embodiments, the extraction of an ROI edge may include one ormore of the following operations. The initial image may be denoised. Thedenoised initial image may be pre-processed based on a gradienttransform. The ROI edge may be detected.

In some embodiments, the detection of the ROI edge may include one ormore of the following operations. The ROI edge may be detected based onan OTSU algorithm or an iterative algorithm.

In some embodiments, the extraction of an ROI may include one or more ofthe following operations. The ROI may be segmented based on an OTSUalgorithm or a watershed algorithm.

In some embodiments, the method may further include one or more of thefollowing operations. The initial image may be transformed into alog-domain image.

In some embodiments, the low frequency sub-image may include apredetermined region including a plurality of gray levels, and thetransformation of the layer may include one or more of the followingoperations. The plurality of gray levels of the predetermined region maybe transformed.

In some embodiments, the transformation of the plurality of gray levelsof the predetermined region may include one or more of the followingoperations. A reference edge in the low frequency sub-image may bedetermined. A characteristic curve may be determined based on the lowfrequency sub-image. The characteristic curve may illustrate therelationship between a distance and a gray level corresponding to thedistance, wherein the distance may be a distance between a first elementin the low frequency sub-image and a second element in the referenceedge, and the first element may correspond to the second element. Thegray level may be determined based on the plurality of gray levels. Atransformation curve may be determined based on the characteristiccurve, wherein the transformation curve may illustrate the relationshipbetween the gray level before transformation and the gray level aftertransformation. The plurality of gray levels of the predetermined regionmay be updated based on the transformation curve.

In some embodiments, the determination of a transformation curve mayinclude one or more of the following operations. The characteristiccurve may be divided into N characteristic curve segments. Ntransformation curve segments may be determined based on the Ncharacteristic curve segments, wherein a characteristic curve segmentmay correspond to a transformation curve segment. The transformationcurve may be generated based on the N transformation curve segments.

In some embodiments, the determination of N transformation curvesegments may include one or more of the following operations. For an xthtransformation curve segment of the N transformation curve segments, aslope of the xth transformation curve segment may be calculated based onthe gray level of a predetermined point in the characteristic curve, agray level of the starting point of an xth characteristic curve segment,and a gray level of the end point of the xth characteristic curvesegment. The xth characteristic curve segment may correspond to the xthtransformation curve segment, wherein x may be an integer, 1≦x≦N. Thedetermination of a gray level of the starting point in the xthtransformation curve segment may include one or more of the followingoperations. If x=1, the gray level of the starting point in the xthcharacteristic curve segment may be designated as the gray level of thestarting point in the xth transformation curve segment. If 1<x≦N, thegray level of the starting point in the xth transformation curve segmentmay be determined based on the gray level of the starting point of the(x−1)th transformation curve segment and a gray level variation of the(x−1)th characteristic curve segment.

In some embodiments, the determination of a transformation curve mayfurther include one or more of the following operations. A gray levelrange of the characteristic curve may be determined, wherein the graylevel range may be a range within which at least one gray level is to betransformed, and the gray level range may correspond to a portion of thecharacteristic curve. The maximum value or minimum value of the graylevel range may be designated as the gray level of the predeterminedpoint in the characteristic curve.

In some embodiments, the decomposition of the target image may includeone or more of the following operations. The target image may bedecomposed into L layers based on a first decomposition, wherein eachlayer of the L layers may include a low frequency sub-image and a highfrequency sub-image, L≧1. The target image may be decomposed into L′+Nimage layers based on a second decomposition, wherein each layer of theL′+N layers may include a low frequency sub-image and a high frequencysub-image, L′≧1, and N≧1.

In some embodiments, L may be equal to L′.

In some embodiments, the first decomposition may be Laplace transform,and the second decomposition may be wavelet transform.

In some embodiments, the reconstruction of the transformed layer mayinclude one or more of the following operations. The low frequencysub-image of the Lth layer generated from the first decomposition may beupdated, based on the low frequency sub-image of the L′th layergenerated from the second decomposition. The composite image may bereconstructed, based on the high frequency sub-images of the L layersgenerated by the first decomposition and the updated low frequencysub-image of the Lth layer.

In some embodiments, the method may further include one or more of thefollowing operations. The high frequency sub-images of the L layersgenerated by the first decomposition may be enhanced.

In some embodiments, the reconstruction of the composite image mayinclude one or more of the following operations. For each of a pluralityof iterations, the low frequency sub-image of the (L−i)th layer may beup-sampled. For each of a plurality of iterations, the low frequencysub-image of the (L−i−1)th layer may be updated, based on the up-sampledlow frequency sub-image of the (L−i)th layer and the high frequencysub-image of the (L−i)th layer, 0≦i≦L−1. For each of a plurality ofiterations, the composite image may be reconstructed, based on theupdated low frequency sub-image of the first layer and the highfrequency sub-image of the first layer.

In some embodiments, the up-sampling of the low frequency sub-image ofthe (L−i)th layer may include one or more of the following operations.The low frequency sub-image of the (L−i)th layer may be up-sampled,based on a bilinear interpolation or a cubic interpolation.

In some embodiments, the method may further include one or more of thefollowing operations. The low frequency sub-image of the L′th layergenerated by the second decomposition may be updated, based on the lowfrequency sub-image of the (L′+N)th layer generated by the seconddecomposition and the high frequency sub-images of the (L′+1)th layerthrough the (L′+N)th layer generated from the second decomposition.

In some embodiments, the high frequency sub-image may include aplurality of elements, and the transformation of the layer may includeone or more of the following operations. A weight image for the highfrequency sub-image may be generated, wherein the weight image mayinclude a plurality of weights corresponding to the plurality ofelements. The high frequency sub-image may be updated, based on theweight image.

In some embodiments, the high frequency sub-image may include a firstclass of elements and a second class of elements, and the generation ofthe weight image may include one or more of the following operations. Agray level range of the first class of elements may be determined in thehigh frequency sub-image. A gray level range of the second class ofelements may be determined in the high frequency sub-image, based on thegray level range of the first class of elements. The gray level range ofthe first class of elements may be mapped into [0, 1]. Weighting factorsfor the first class of elements may be determined, based on the mappedgray level range of the first class of elements. The gray level range ofthe second class of elements may be mapped into (1, G], wherein G may bea predetermined value. Weighting factors for the second class ofelements may be determined, based on the mapped gray level range of thesecond class of elements. The weight image may be generated, based onthe weighting factors for the first class of elements and the weightingfactors for the second class of elements.

In some embodiments, the determination of a gray level range of thefirst class of elements may include one or more of the followingoperations. An initial gray level range of the first class of elementsmay be determined, based on a gray level threshold. The initial graylevel range of the first class of elements may be modified. The initialgray level range of the first class of elements may be adjusted, basedon the modified gray level range of the first class of elements.

In some embodiments, the adjustment of the initial gray level range ofthe first class of elements may include one or more of the followingoperations. A first threshold may be calculated, based on the modifiedgray level range of the first class of elements. The gray level range ofthe first class of elements may be determined as [0, the firstthreshold].

In some embodiments, the transformation of the high frequency sub-imagemay include one or more of the following operations. The gray levels ofthe high frequency sub-image may be multiplied by that of the weightimage.

In some embodiments, the transformation of the layer may include one ormore of the following operations. The high frequency sub-image may betransformed by linear/nonlinear enhancement, or denoising.

In some embodiments, the transformation of the layer may include one ormore of the following operations. The low frequency sub-image may betransformed by linear/nonlinear enhancement, or denoising.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2-A is a schematic diagram illustrating an exemplary imageprocessing system according to some embodiments of the presentdisclosure;

FIG. 2-B is a flowchart of an exemplary process for processing an imageaccording to some embodiments of the present disclosure;

FIG. 3-A is a schematic diagram illustrating an exemplary imageacquisition block according to some embodiments of the presentdisclosure;

FIG. 3-B is a flowchart of an exemplary process for acquiring an imageaccording to some embodiments of the present disclosure;

FIG. 4-A is a flowchart of an exemplary process for decomposing an imageaccording to some embodiments of the present disclosure;

FIG. 4-B is a schematic diagram illustrating exemplary L layersdecomposed by a first decomposition unit according to some embodimentsof the present disclosure;

FIG. 4-C is a schematic diagram illustrating exemplary L′+N layersdecomposed by a second decomposition unit according to some embodimentsof the present disclosure;

FIG. 5-A is a schematic diagram illustrating an exemplary gray leveltransformation unit according to some embodiments of the presentdisclosure;

FIG. 5-B is a flowchart of an exemplary process for transforming animage according to some embodiments of the present disclosure;

FIG. 6 is a schematic diagram illustrating an exemplary characteristiccurve according to some embodiments of the present disclosure;

FIG. 7 is a schematic diagram illustrating an exemplary characteristiccurve segmented into a plurality of characteristic curve segmentsaccording to some embodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating an exemplary transformationcurve according to some embodiments of the present disclosure;

FIG. 9-A is a schematic diagram illustrating an exemplary weighttransformation unit according to some embodiments of the presentdisclosure;

FIG. 9-B is a flowchart illustrating an exemplary process fortransforming a target image based on a weight image according to someembodiments of the present disclosure;

FIG. 9-C is a flowchart illustrating an exemplary process fordetermining a weight image according to some embodiments of the presentdisclosure;

FIG. 10 is a schematic diagram illustrating an exemplary process forgenerating a weight image;

FIG. 11-A is a flowchart of an exemplary process for reconstructing acomposite image based on one layer according to some embodiments of thepresent disclosure;

FIG. 11-B is a flowchart of an exemplary process for reconstructing alow frequency sub-image of L′th layer generated from the seconddecomposition according to some embodiments of the present disclosure;and

FIG. 11-C is a flowchart of an exemplary process for reconstructing acomposite image based on L layers generated from the first decompositionaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “module,” and/or“unit” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theymay achieve the same purpose.

It will be understood that when a device, unit, or module is referred toas being “on,” “connected to/with,” or “coupled to/with” another device,unit, or module, it may be directly on, connected or coupled to, orcommunicate with the other device, unit, or module, or an interveningdevice, unit, or module may be present, unless the context clearlyindicates otherwise. As used herein, the term “and/or” includes any andall combinations of one or more of the associated listed items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

For illustration purposes, the following description is provided to helpbetter understanding of an image processing or enhancement method orsystem. It should be noted that “image” may refer to a medial image, astatic picture, or a video frame. It is understood that this is notintended to limit the scope the present disclosure. For persons havingordinary skills in the art, a certain amount of variations, changesand/or modifications may be deducted under guidance of the presentdisclosure. Those variations, changes and/or modifications do not departfrom the scope of the present disclosure.

FIG. 1 is a block diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure. The imagingsystem may produce an image of an object. As illustrated, the imagingsystem may include an imaging device 110, a controller 120, an imageprocessing system 130, a storage 140, and an input/output device 150.

The imaging device 110 may scan an object, and generate a plurality ofdata relating to the object. In some embodiments, the imaging device 110may be a medical imaging device, for example, a PET device, a SPECTdevice, a CT device, an MM device, an X-ray photography equipment (e.g.,a full-field digital mammography (FFDM)), a digital breast tomosynthesis(DBT) equipment, a Digital Subtraction Angiography (DSA) system, aMagnetic Resonance Angiography (MRA) system, Computed TomographyAngiography (CTA), a Digital Radiography (DR) system, or the like, orany combination thereof (e.g., a PET-CT device, a PET-MRI device, or aSPECT-MRI device).

In some embodiments, the imaging device 110 may include a scanner toscan an object and obtain information related to the object. In someembodiments, the imaging device 110 may be a radioactive scanningdevice. The radioactive scanning device may include a radioactive sourceto emit radioactive rays to the object being scanned. The radioactiverays may include, for example, particle rays, photon rays, or the like,or any combination thereof. The particle rays may include neutron,proton, electron, α-ray, μ-meson, heavy ion, or the like, or anycombination thereof. The photon rays may include X-ray, γ-ray,ultraviolet, laser, or the like, or any combination thereof.

In some embodiments, the photon ray may be X-ray, and the imaging device110 may be a CT system, a digital radiography (DR) system, amulti-modality system, or the like, or any combination thereof.Exemplary multi-modality system may include a CT-PET system, a SPECT-MRIsystem, or the like. In some embodiments, the imaging device 110 mayinclude an X-ray generating unit (not shown) and an X-ray detecting unit(not shown). In some embodiments, the imaging device 110 may include aphoton detector to capture the photon generated from the object beingscanned. In some embodiments, the photon detector may include ascintillator, and/or a photodetector, and the imaging device 110 may bea PET system, or a multi-modality system (e.g., a PET-CT system, aPET-MM system, or the like). In some embodiments, the imaging device 110may include a main magnetic field generator, a plurality of gradientcoils, a radiofrequency (RF) transmitter, and/or an RF receiver. Theimaging device 110 may be an MM system, or a multi-modality system(e.g., a PET-MRI system, a SPECT-MRI system, or the like).

The controller 120 may control the imaging device 110, the imageprocessing system 130, the storage 140, and/or the input/output device150. The controller 120 may control the communication among the imagingdevice 110, the image processing system 130, the storage 140, theinput/output device 150, and/or the network 160. The controller 120 mayreceive information from or send information to the imaging device 110,the storage 140, the input/output device 150, and/or the imageprocessing system 130. For example, the controller 120 may receivecommands from the input/output device 150 provided by a user. Thecontroller 130 may process information input by a user via theinput/output unit 150 and transform the information into one or morecommands. As another example, the controller 120 may control the imagingdevice 110, the input/output device 150, and/or the image processingsystem 130 according to the received commands or transformed commands.As still another example, the controller 120 may receive image signalsor data related to an object from the imaging device 110. As stillanother example, the controller 120 may send image signals or data tothe image processing system 130. As still another example, thecontroller 120 may receive processed data or constructed image from theimage processing system 130. As still another example, the controller120 may send processed data or constructed image to the input/outputdevice 150 for displaying. As still another example, the controller 120may send processed data or constructed image to the storage 140 forstoring. As still another example, the controller 120 may readinformation from the storage 140 and transmit the information to theimage processing system 130 for processing. In some embodiments, thecontroller 120 may include a computer, a program, an algorithm, asoftware, a storage device, one or more interfaces, etc. Exemplaryinterfaces may include the interfaces of the imaging device 110, theinput/output device 150, the image processing system 130, the storage140, and/or other modules or units in the imaging system.

In some embodiments, the controller 120 may receive a command providedby a user including, for example, an imaging technician, a doctor, etc.Exemplary commands may relate to the scan duration, the location of theobject, the location of a couch on which the object lies, the workingcondition of the imaging device 110, a specific parameter that may beused in image processing, or the like, or any combination thereof. Insome embodiments, the controller 120 may control the image processingsystem 130 to select different algorithms to process an image.

The image processing system 130 may process information received fromthe imaging device 110, the controller 120, the storage 140, the network160, and/or the input/output device 150. In some embodiments, the imageprocessing system 130 may generate one or more images based on theinformation. In some embodiments, the image processing system 130 mayprocess one or more images. The image(s) processed by the imageprocessing system 130 may include 2D image(s) and/or 3D image(s). Theimage processing system 130 may deliver the images to the input/outputdevice 150 for display, or to the storage 140 for storing. In someembodiments, the image processing system 130 may perform operationsincluding, for example, image preprocessing, image reconstruction, imageenhancement, image correction, image composition, lookup table creation,or the like, or any combination thereof. In some embodiments, the imageprocessing system 130 may process data based on an algorithm including,for example, filtered back projection algorithm, fan-beamreconstruction, iterative reconstruction, gray level transformation,wave filtering, wavelet transform, Laplace transform, or the like, orany combination thereof.

In some embodiments, the image processing system 130 may include one ormore processors to perform processing operations disclosed in thisdisclosure. The processor(s) may include a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), or any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

In some embodiments, the image processing system 130 may also include amemory configured to store data and/or instructions. In someembodiments, the memory may include a mass storage, a removable storage,a volatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. Exemplary mass storage may include amagnetic disk, an optical disk, a solid-state drives, etc. Exemplaryremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (PEROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), a digital versatile disk ROM,etc. In some embodiments, the memory may store one or more programsand/or instructions that may be executed by the processor(s) of theimage processing system 130 to perform exemplary methods described inthe this disclosure. For example, the memory may store program(s) and/orinstruction(s) executed by the processor(s) of the image processingsystem 130 to decompose an image, transform a decomposed image, and/orreconstruct an image. For example, a ROM may store a decompositionalgorithm (e.g., Laplace algorithm) for the image processing system 130to decompose an image.

In some embodiments, image data regarding a region of interest (ROI) maybe processed by the image processing system 130. In some embodiments,the image processing system 130 may improve image quality, enhance imagecontrast, reduce or remove image artifact(s), reduce or eliminate imagenoise, and/or enhance the ROI edge information. The image artifact(s)may include oscillating artifact, speckle artifact, or the like, or anycombination thereof. The ROI edge information may refer to theinformation (e.g., gray level, contrast, brightness, etc.) regarding theedge of a region of interest, for example, a breast edge, a tumor edge,or the like, or any combination thereof.

In some embodiments, the image processing system 130 may generate acontrol signal relating to the configuration of the imaging device 110.In some embodiments, the result generated by the image processing system130 may be provided to other modules or units in the system including,for example, a database (not shown), a terminal (not shown) via thenetwork 160. In some embodiments, the data from the image processingsystem 130 may be transmitted to the storage 140 for storing.

The storage 140 may store information sent from the imaging device 110,the controller 120, the image processing system 130, the input/outputdevice 150, and/or an external data storage device via the network 160.The information stored may include a numerical value, a signal, animage, information of an object, an instruction, an algorithm, or thelike, or a combination thereof. The storage 140 may refer to a systemstorage (e.g., a disk) that is provided integrally (i.e. substantiallynon-removable), or a storage that is removably connectable to the systemvia, for example, a port (e.g., a UBS port, a firewire port, etc.), adrive (a disk drive, etc.), etc. The storage 140 may include, forexample, a hard disk, a floppy disk, selectron storage, random accessmemory (RAM), dynamic random access memory (DRAM), static random accessmemory (SRAM), bubble memory, thin film memory, magnetic plated wirememory, phase change memory, flash memory, a cloud disk, or the like, ora combination thereof. The storage 140 may be connected to orcommunicate with one or more components of the imaging system. In someembodiments, the storage 140 may be operationally connected with one ormore virtual storage resources (e.g., cloud storage, a virtual privatenetwork, other virtual storage resources, etc.) via the network 160.

The input/output device 150 may receive or output information. In someembodiments, the input/output device 150 may include a terminal, akeyboard, a touch screen, a cursor control device, a remote controller,or the like, or any combination thereof. The terminal may include, forexample, a control panel, a mobile device (e.g., a smart phone, atablet, a laptop computer, or the like), a personal computer, otherdevices, or the like, or any combination thereof. The other devices mayinclude a device that may work independently, or a processing unit orprocessing module assembled in another device. The cursor control devicemay include a mouse, a trackball, or cursor direction keys tocommunicate direction information and command selections to, forexample, the image processing system 130 and to control cursor movementon a display device.

The input and/or output information may include programs, software,algorithms, data, text, number, images, voices, or the like, or anycombination thereof. For example, a user may input some initialparameters or conditions to initiate an imaging process. As anotherexample, some information may be imported from an external resourceincluding, for example, a floppy disk, a hard disk, a wired terminal, awireless terminal, or the like, or any combination thereof. In someembodiments, the input and/or output information may further includealphanumeric and/or other keys that may be inputted via a keyboard, atouch screen (for example, with haptics or tactile feedback), a voiceinput, an image input, an eye tracking input, a brain monitoring system,or any other comparable input mechanism. The output information may betransmitted to the storage 140, a display (not shown), a printer (notshown), a computing device, or the like, or a combination thereof.

In some embodiments, the input/output device 150 may include a userinterface. The user interface may be a user interaction interface, agraphical user interface (GUI), or a user-defined interface, etc. Thegraphical user interface may allow a user to interact with othercomponents (e.g., imaging device 110 and/or controller 120). Forexample, the graphical user interface may facilitate a user to inputparameters, and intervene in the image processing procedure. In someembodiments, the input/output device 150 may include a display. Thedisplay may include a liquid crystal display (LCD), a light emittingdiode (LED) based display, a flat panel display or curved screen (ortelevision), a cathode ray tube (CRT), a 3D display, a plasma displaypanel, or the like, or any combination thereof.

Network 160 may facilitate communications among imaging device 110,controller 120, image processing system 130, storage 140, and/orinput/output device 150. For example, information may be transmitted vianetwork 160 from imaging device 110 to image processing system 130. Asanother example, information processed and/or generated by imageprocessing system 130 may be transmitted via network 160 to storage 140and/or input/output device 150.

In some embodiments, network 160 may be a wired network, a nanoscalenetwork, a near field communication (NFC), a body area network (BAN), apersonal area network (PAN, e.g., a Bluetooth, a Z-Wave, a Zigbee, awireless USB), a near-me area network (NAN), a local wireless network, abackbone, a metropolitan area network (MAN), a wide area network (WAN),an internet area network (IAN, or cloud), or the like, or anycombination thereof. Known communication techniques that provide amedium for transmitting data between separate are also contemplated. Insome embodiments, the network 160 may be a single network or acombination of a variety of networks. The network 160 may include butnot limited to local area network, wide area network, public network,private network, wireless LAN, virtual network, urban metropolitan areanetwork, public switch telephone network, or a combination thereof. Insome embodiments, the network 160 may include various network accesspoints, for example, wired or wireless access points, base station ornetwork switch points, by which data source may connect with the network160 and the information may be transmitted via the network 160.

In some embodiments, two or more of the imaging device 110, thecontroller 120, the image processing system 130, the storage 140, andthe input/output device 150 may be connected to or communicate with eachother directly. In some embodiments, the imaging device 110, thecontroller 120, the image processing system 130, the storage 140, andthe input/output device 150 may be connected to or communicate with eachother via the network 160. In some embodiments, the imaging device 110,the controller 120, the image processing system 130, the storage 140,and the input/output device 150 may be connected to or communicate witheach other via an intermediate unit (not shown). The intermediate unitmay be a visible component or an invisible field (radio, optical, sonic,electromagnetic induction, etc.). The connection between different unitsmay be wired or wireless. The wired connection may include using a metalcable, an optical cable, a hybrid cable, an interface, or the like, orany combination thereof. The wireless connection may include using aLocal Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, aZigBee, a Near Field Communication (NFC), or the like, or anycombination thereof. The network 160 that may be used in connection withthe present system described herein are not exhaustive and are notlimiting.

The imaging system described herein is merely provided for illustratingan example, and not intended to limit the scope of the presentapplication. The imaging system may find its applications in differentfields such as, for example, medicine or industry. The imaging device110 may be used in internal inspection of components including, forexample, flaw detection, security scanning, failure analysis, metrology,assembly analysis, void analysis, wall thickness analysis, or the like,or any combination thereof. To those skilled in the art, afterunderstanding the basic principles of the connection between differentcomponents, the components and connection between the units may bemodified or varied without departing from the principles. Themodifications and variations are still within the scope of the currentapplication described above. In some embodiments, these components maybe independent, and in some embodiments, part of the components may beintegrated into one component to work together.

FIG. 2-A is a schematic diagram illustrating an exemplary imageprocessing system 130 according to some embodiments of the presentdisclosure. As shown in FIG. 2-A, the image processing system 130 mayinclude an image acquisition block 201, a decomposition block 202, atransformation block 203, and a reconstruction block 204.

In some embodiments, image acquisition block 201 may acquire an image.The image may refer to a 2D image or 3D image. A 2D image may include aplurality of pixels. A 3D image may include a plurality of voxels. Apixel/voxel may have a corresponding value including, for example,brightness, color, gray level, or the like, or any combination thereof.For brevity, a pixel or a voxel may be referred to as an element. Anelement may refer to a pixel of a 2D image, or a voxel of a 3D image. Insome embodiments, image acquisition block 201 may obtain a signal and/ordata representative of an image. The signal may take any of a variety offorms, including an electro-magnetic form, an optical form, or the like,or any suitable combination thereof. In some embodiments, the data ofthe image may include raw data, processed data, control data,interaction data, image data, video data, analog data, digital data, orthe like, or any combination thereof. In some embodiments, the imageacquired may include an initial image, a region of interest (ROI) of theinitial image, any image generated in an image processing procedure, orthe like, or any combination thereof.

The initial image may refer to an image initially acquired by imageacquisition block 201. In some embodiments, the initial image may begenerated by imaging device 110. For example, the initial image may bean original image that may be captured by a CT system, a PET system, anMM system, or the like, or any combination thereof. In some embodiments,the initial image may be obtained from storage 140, input/output device150, or an external data storage device via network 160. For example,the initial image may be a processed image that may be previously storedin storage 140. In some embodiments, the initial image may be processedby image acquisition block 201, and a target image may be generated. Thetarget image may refer to an image that may be decomposed, transformed,reconstructed, and/or enhanced by the image processing system 130.

The ROI image may include a region of interest of the initial image. Theregion of interest may refer to a portion of the initial image that mayinclude information of interest including, for example, a tissue ofinterest, an organ of interest, a background of interest, a lesion ofinterest, any region of interest in the initial image, or the like, orany combination thereof. In some embodiments, a ROI may be extractedfrom the initial image, and a ROI image may be obtained. For example, anorgan of interest may be extracted from the initial image, and an organimage may be generated. The organ image may include a lung, a breast, apart of skeleton, a part of muscle, an eye, any part in a body, or thelike, or any combination thereof. Merely by way of example, the organimage may be a breast image extracted from a chest CT image.

In some embodiments, the image acquired by image acquisition block 201may be obtained from imaging device 110, storage 140, input/outputdevice 150, an external data storage device via network 160, anycomponent of image processing system 130, or the like, or anycombination thereof. In some embodiments, the image acquired may beprocessed by decomposition block 202, transformation block 203, and/orreconstruction block 204. For example, the image acquired may bedecomposed by decomposition block 202 in order to enhance the image. Insome embodiments, the image acquired may be stored in storage 140,displayed by input/output device 150, transmitted to a terminal or anexternal data storage device via network 160, or the like, or anycombination thereof.

In some embodiments, decomposition block 202 may decompose a targetimage acquired by image acquisition block 201. As shown in FIG. 2-A,decomposition block 202 may include a first decomposition unit 202-1, asecond decomposition unit 202-2, and/or an Nth decomposition unit 202-N,in which N may be an integer. In some embodiments, at least twodecomposition units may use a same decomposition algorithm. In someembodiments, at least two decomposition units may use differentdecomposition algorithms.

In some embodiments, decomposition block 202 may decompose the imageinto one or more layers. In some embodiments, image decomposition mayrefer to dividing or decomposing the image into one or more layers ofimage based on the gray levels of the elements of the image, frequenciesof the image, etc. A layer of image may include two or more sub-images.For example, a layer of image may include a low frequency sub-image anda high frequency sub-image. In some embodiments, the low frequencysub-image and the high frequency sub-image may be determined based onone or more frequency thresholds. For example, a sub-image withfrequencies lower than or equal to a frequency threshold T_(f) may bedetermined as the low frequency sub-image. As another example, asub-image with frequencies greater than or equal to the frequencythreshold T_(f) may be determined as the high frequency sub-image. Thethreshold T_(f) may be predetermined according to a default setting ofthe image processing system 130 or determined by a user through a GUI ofthe input/output device 150. In some embodiments, the threshold T_(f)may be adjusted based on a processing efficiency of the image in theimage processing system 130. In some embodiments, decomposition block202 may further decompose a sub-image into one or more layers. Forexample, decomposition block 202 may decompose a low frequency sub-imageor a high frequency sub-image into one or more layers. In someembodiments, decomposition block 202 may decompose the low frequencysub-image in a first layer into a low frequency sub-image and a highfrequency sub-image in a second layer. In some embodiments,decomposition block 202 may decompose the low frequency sub-image in asecond layer into a low frequency sub-image and a high frequencysub-image in a third layer.

In some embodiments, first decomposition unit 202-1 may decompose theimage (or sub-image) based on a first decomposition algorithm. In someembodiments, second decomposition unit 202-2 may decompose the image (orsub-image) based on a second decomposition algorithm. In someembodiments, Nth decomposition unit 202-N may decompose the image (orsub-image) based on an Nth decomposition algorithm. In some embodiments,the decomposition algorithm may include a wavelet transform, a bilateralfiltering, a Fourier algorithm, a discrete cosine transformation, aLaplace transform, any algorithm capable of decomposing an image, or thelike, or any combination thereof. The wavelet transform may include acontinuous wavelet transform, a discrete wavelet transform (DWT), a fastwavelet transform (FWT), a lifting scheme & generalized lifting scheme,a wavelet packet decomposition, a stationary wavelet transform, afractional Fourier transform, a fractional wavelet transform, or thelike, or any combination thereof. In some embodiments, at least twodecomposition units may use different decomposition algorithms. Forexample, first decomposition unit 202-1 may decompose the image based ona Laplace transform, and second decomposition unit 202-2 may decomposethe image based on a wavelet transform. As another example, firstdecomposition unit 202-1 may decompose the image based on a wavelettransform, and second decomposition unit 202-2 may decompose the imagebased on a Laplace transform.

In some embodiments, first decomposition unit 202-1 may decompose theimage (or sub-image) into one or more layers based on a firstdecomposition algorithm. For example, first decomposition unit 202-1 maydecompose the image (or sub-image) into two or more sub-images in afirst layer based on a first decomposition algorithm. In someembodiments, first decomposition unit 202-1 may further decompose asub-image in the first layer into two or more sub-images in a secondlayer based on the first decomposition algorithm. In some embodiments,first decomposition unit 202-1 may further decompose a sub-image in theLth layer into two or more sub-images in a (L+1)th layer based on thefirst decomposition algorithm, in which L may be an integer larger than2. For example, first decomposition block 202-1 may decompose the image(or sub-image) into 3 layers based on a wavelet transform, in which eachlayer may include a high frequency sub-image and a low frequencysub-image, the sub-images in the second layer may be generated from thelow frequency sub-image in the first layer, and the sub-images in thethird layer may be generated from the low frequency sub-image in thesecond layer.

In some embodiments, second decomposition unit 202-2 may decompose theimage (or sub-image) into one or more layers based on a seconddecomposition algorithm. For example, second decomposition unit 202-2may decompose the image (or sub-image) into two or more sub-images in afirst layer based on a second decomposition algorithm. In someembodiments, second decomposition unit 202-2 may further decompose asub-image in the first layer into two or more sub-images in a secondlayer based on the second decomposition algorithm. In some embodiments,second decomposition unit 202-2 may further decompose a sub-image in theLth layer into two or more sub-images in a (L+1)th layer based on thesecond decomposition algorithm, in which L may be an integer larger than2. For example, second decomposition block 202-2 may decompose the image(or sub-image) into 5 layers based on a Laplace transform, in which eachlayer may include a high frequency sub-image and a low frequencysub-image. The sub-images in the second layer may be generated from thelow frequency sub-image in the first layer. The sub-images in the thirdlayer may be generated from the low frequency sub-image in the secondlayer. The sub-images in the fourth layer may be generated from the lowfrequency sub-image in the third layer. The sub-images in the fifthlayer may be generated from the low frequency sub-image in the fourthlayer.

In some embodiments, Nth decomposition unit 202-N may decompose theimage (or sub-image) into one or more layers based on an Nthdecomposition algorithm. For example, Nth decomposition unit 202-N maydecompose the image (or sub-image) into two or more sub-images in afirst layer based on the Nth decomposition algorithm. In someembodiments, Nth decomposition unit 202-N may further decompose asub-image in the first layer into two or more sub-images in a secondlayer based on the Nth decomposition algorithm. In some embodiments, Nthdecomposition unit 202-N may further decompose a sub-image in the Lthlayer into two or more sub-images in a (L+1)th layer based on the Nthdecomposition algorithm, in which L may be an integer larger than 2.

In some embodiments, an image may be decomposed by first decompositionunit 202-1 into one or more layers. In some embodiments, a sub-image(s)in one of the layers generated by first decomposition unit 202-1 may befurther decomposed by second decomposition unit 202-2 into one or morelayers including sub-images. In some embodiments, a sub-image generatedby the second decomposition unit 202-2 may further be decomposed by Nthdecomposition unit 202-N. For example, first decomposition unit 202-1may decompose the image into a first layer including a low frequencysub-image and a high frequency sub-image. The low frequency sub-imagegenerated by first decomposition unit 202-1 may be further decomposed bysecond decomposition unit 202-2 into a low frequency sub-image and ahigh frequency sub-image of a second layer. The low frequency sub-imagein the second layer generated by second decomposition unit 202-2 may bedecomposed by Nth decomposition unit 202-N into a low frequencysub-image and a high frequency sub-image. In some embodiments,decomposition block 202 may decompose an image into no less than 2layers (e.g., 5 layers), based on a same decomposition algorithm ordifferent decomposition algorithms.

In some embodiments, the image(s) and/or sub-image(s) generated bydecomposition block 202 may be acquired by image acquisition block 201,processed by transformation block 203, provided to reconstruction block204, stored in storage 140, transmitted through network 160, or thelike, or any combination thereof. For example, two or more sub-imagesmay be reconstructed by reconstruction block 204 into a processed image.

Transformation block 203 may transform a target image or a sub-image.The image (or sub-image) to be transformed may be obtained from imageacquisition block 201, or decomposition block 202. In some embodiments,transformation block 203 may change the value(s) of one or more elementsin the image (or sub-image). As shown in FIG. 2-A, transformation block203 may include a gray level transformation unit 203-1, a weighttransformation unit 203-2, an enhancement unit 203-3, and an up-samplingunit 203-4.

Gray level transformation unit 203-1 may transform gray level(s) of oneor more elements in a target image (or sub-image). In some embodiments,gray level transformation unit 203-1 may transform gray level(s) of oneor more elements in a target image (or sub-image) to adjust the graylevel(s) of the elements of an ROI, improve the quality of the targetimage (or sub-image), reduce noise, or the like, or any combinationthereof. In some embodiments, gray level transformation unit 203-1 maytransform the image (or sub-image) based on one or more transformationtechniques. The transformation technique may be based on atransformation function, a transformation rule, a transformation curve,or the like, or any combination thereof. FIG. 5-A and FIG. 8 illustrateexemplary transformation curves. In some embodiments, gray leveltransformation unit 203-1 may transform different gray levels based ondifferent transformation techniques. In some embodiments, the gray levelmay be represented by an integer. The gray level may be limited within arange. For example, the range of the gray level may be between 0 and 1,or between 1 and 255.

In some embodiments, the gray level may represent an intensity of anelement in a grayscale image. In some embodiments, the gray level mayrepresent an intensity of an element in a single color channel of acolor image (or sub-image). The color image (or sub-image) may have acolor space including a red/green/blue (RGB) space, ahue/saturation/luminance (HIS) space, a hue/saturation/lightness (HSL)space, a hue/saturation/value (HSV) space, a commission internationalede l'Eclairage (CIE) space, a hue/saturation/intensity (HSI) space, orany other color space that may present human color perception, or anycombination thereof. For example, the gray level may represent theintensity in the red channel of the color image in the RGB space.

In some embodiments, the transformation technique(s) may be modifiedbased on a transformation result generated by gray level transformationunit 203-1, stored in storage 140, or obtained from network 160. Thetransformation technique(s) may be used to compress or enhance the graylevel(s) of one or more elements. In some embodiments, a transformationcurve may be generated based on a characteristic curve. Thecharacteristic curve may be determined based on an initial image, anROI, an ROI edge, an ROI image, or a sub-image. For example, thecharacteristic curve may be determined based on a low frequencysub-image (e.g., a low frequency sub-image generated by decompositionblock 202). In some embodiments, the characteristic curve may berepresented by minimum distance(s) in a horizontal axis, andcorresponding gray level(s) of elements in the low frequency sub-imagein a vertical axis. In some embodiments, the horizontal axis may referto the X axis of a Cartesian coordinate. In some embodiments, thevertical axis may refer to the Y axis of the Cartesian coordinate.

Weight transformation unit 203-2 may transform a gray level of anelement in a target image (or sub-image) based on one or more weightingfactors, or a weight image. In some embodiments, weight transformationunit 203-2 may transform gray level(s) to reduce noise, reduce speckleartifact, improve image contrast, or the like, or any combinationthereof. In some embodiments, the weight image may be determined basedon the target image (or sub-image). For example, a target sub-image maybe a high frequency sub-image generated by decomposition block 202, andthe weight image may be generated based on the values (e.g., the graylevel(s), the brightness value(s), etc.) of one or more elements in thehigh frequency sub-image. As another example, a sub-image may be a lowfrequency sub-image generated by decomposition block 202, and the weightimage may be generated based on the values (e.g., the gray level(s), thebrightness value(s), etc.) of one or more elements in the low frequencysub-image.

Enhancement unit 203-3 may enhance a target image (or sub-image). Insome embodiments, enhancement unit 203-3 may enhance the image (orsub-image) based on a linear enhancement and/or a nonlinear enhancement.The linear enhancement may include a Max-Min contrast technique,percentage contrast technique and piecewise contrast technique, or thelike, or any combination thereof. The nonlinear enhancement may includehistogram equalization technique, adaptive histogram equalizationtechnique, homomorphic filter technique and unsharp mask, or the like,or any combination thereof. In some embodiments, enhancement unit 203-3may enhance a target sub-image generated by decomposition block 202. Forexample, enhancement unit 203-3 may enhance a high frequency sub-imageor a low frequency sub-image decomposed by first decomposition unit202-1, second decomposition unit 202-2, or Nth decomposition unit 202-N.

Up-sampling unit 203-4 may up-sample a target image (or sub-image). Insome embodiments, up-sampling unit 203-4 may up-sample the target image(or sub-image) based on one or more interpolation processes including,for example, piecewise constant interpolation, linear interpolation,polynomial interpolation, spine interpolation, or the like, or anycombination thereof. In some embodiments, one or more sub-images may beup-sampled by up-sampling unit 203-4. For example, the high frequencysub-image(s) and/or the low frequency sub-image(s) generated bydecomposition block 202 may be interpolated by up-sampling unit 203-4.

In some embodiments, the image and/or sub-image transformed bytransformation block 203 may be decomposed by decomposition block 202,reconstructed by reconstruction block 204, stored in storage 140,displayed by input/output device 150, or transmitted through network160. In some embodiments, two or more sub-images transformed by the sameunit in transformation block 203 may be reconstructed into a compositeimage. For example, two or more sub-images transformed by gray leveltransformation unit 203-1 may be reconstructed by reconstruction block204 into a composite image. In some embodiments, the image and/orsub-image transformed by two or more units in the transformation block203 may be reconstructed. For example, a low frequency sub-magetransformed by gray level transformation unit 203-1 and a high frequencysub-image transformed by weight transformation unit 203-2 may bereconstructed into a composite image.

Reconstruction block 204 may reconstruct an image based on two or moreimages (or sub-images). The images (or sub-images) may be obtained fromimage acquisition block 201, decomposition block 202, transformationblock 203, storage 140, input/output device 150, or an external datastorage device via network 160. In some embodiments, reconstructionblock 204 may reconstruct an image based on a technique including, forexample, a filter back projection algorithm, an iterative reconstructionalgorithm, local reconstruction algorithm, multiple additive regressiontree algorithm, Random transform algorithm, Fourier transform algorithm,or the like, or any combination thereof.

FIG. 2-B is a flowchart of an exemplary process for processing an imageaccording to some embodiments of the present disclosure. The process mayinclude acquiring image 211, decomposing image 212, transforming image213, and reconstructing image 214.

In 211, an image may be acquired by image acquisition block 201. Theimage may be acquired from imaging device 110, storage 140, input/outputdevice 150, network 160, any component in image processing system 130,or the like, or any combination thereof. The image acquired may includean initial image, an ROI image, any image generated in an imageprocessing procedure, or the like, or any combination thereof. In someembodiments, information may be extracted from an initial image by imageacquisition block 201, and a target image may be generated. For example,the region of interest may be extracted from an initial image by imageacquisition block 201, and an ROI image may be generated. The initialimage and/or the target image may be processed in the subsequentprocedure.

In some embodiments, the image acquired in 211 may be a 2D image or 3Dimage. In some embodiments, the image acquired may be a grey level imageor color image. In some embodiments, the image acquired may be a medicalimage, for example, a CT image, an MM image, a PET image, or the like,or any combination thereof.

In some embodiments, the image acquired at 211 may be provided todecomposition block 202, reconstruction block 204, input/output device150, or the like, or any combination thereof. For example, a breastimage acquired in 211 may be used to generate one or more sub-images bydecomposition block 202. In some embodiments, the image may be store atstorage 140, an external data storage device via network 160, anycomponent capable of storing, or the like, or any combination thereof.

In 212, the image acquired or generated in 211 may be decomposed. Thedecomposition may be performed by decomposition block 202. In someembodiments, the image may be decomposed into one or more layers. Insome embodiments, decomposition block 202 may use one or moredecomposition algorithms to decompose the image. The decompositionalgorithm may include a bilateral filtering algorithm, a waveletfiltering algorithm, a Laplace transform, an intrinsic imagedecomposition algorithm, or the like, or any combination thereof. Insome embodiments, the image may be decomposed by different decompositionunits. For example, the image may be decomposed by first decompositionunit 202-1 and/or second decomposition unit 202-2. In some embodiments,the image may be decomposed based on a sub-image. For example, the imagemay be decomposed into a first layer including a low frequency sub-imageand a high frequency sub-image by first decomposition unit 202-1. Thelow frequency sub-image in the first layer may be decomposed into asecond layer including a low frequency sub-image and a high frequencysub-image by second decomposition unit 202-2. Similarly, the lowfrequency sub-image in the second layer may be further decomposed intoan Nth layer by Nth decomposition unit 202-N. In some embodiments, oneor more of the sub-images obtained after decomposition may beunder-sampled. For example, if the decomposition block 202 decompose theimage using a wavelet transform or Laplace transform, the sub-imagesobtained may be under-sampled.

In some embodiments, the image may be decomposed by decomposition block202 into L layers. For example, the image may be decomposed by firstdecomposition unit 202-1 into a first layer. The first layer may includea low frequency sub-image and a high frequency image. The low frequencysub-image in the first layer may be further decomposed by firstdecomposition unit 202-1 into a second layer. The low frequencysub-image in the second layer may be further decomposed by firstdecomposition unit 202-1 into a third layer. Likewise, the low frequencysub-image in the (L−1)th layer may be further decomposed by firstdecomposition unit 202-1 into an Lth layer. Similarly, the image may bedecomposed by decomposition block 202 into L′+N layers. For example, theimage may be decomposed by second decomposition unit 202-2 into L′+Nlayer. In some embodiments, the image may be decomposed by two or moreof the decomposition units (e.g., first decomposition unit 202-1, seconddecomposition unit 202-2, Nth decomposition unit 202-N, etc.) into Llayers. For example, the image may be decomposed by first decompositionunit 202-1 into a first layer. The first layer may include a lowfrequency sub-image and a high frequency image. The low frequencysub-image or the high frequency image in the first layer may be furtherdecomposed by second decomposition unit 202-2 into a second layer.Likewise, the low frequency sub-image or the high frequency image in the(L−1)th layer may be further decomposed by Nth decomposition unit 202-Ninto an Lth layer. In some embodiments, two or more of the decompositionunits may use the same decomposition algorithm or differentdecomposition algorithms. In some embodiments, two or more of thedecomposition units may use the same parameter of different parameterswith the same decomposition algorithm.

In 213, the image(s) acquired in 211 and/or the sub-image(s) generatedin 212 may be transformed. Transformation block 203 may perform 213. Insome embodiments, one or more of the images (and/or sub-images) may betransformed by gray level transformation unit 203-1 based on a graylevel transformation curve. The gray level transformation curve may bedetermined by the gray level transformation unit 203-1 based on the graylevels of the elements in the images (and/or sub-images) to betransformed. In some embodiments, one or more of the images (and/orsub-images) may be transformed by weight transformation unit 203-2 basedon a weight image. The weight image may be determined by weighttransformation unit 203-2 based on the images (and/or sub-images) to betransformed. In some embodiments, in 212, one or more of the images(and/or sub-images) may be enhanced by enhancement unit 203-3. Forexample, a high frequency sub-image may be enhanced using a histogramequalization technique. In some embodiments, one or more of the images(and/or sub-images) may be up-sampled by up-sampling unit 203-4. Forexample, a high frequency sub-image may be up-sampled through linearinterpolation. In some embodiments, one or more images (or sub-images)may be replaced by another image (or sub-image). For example, a highfrequency sub-image (or low frequency sub-image) in an xth (x may be aninteger) layer generated based on a first decomposition algorithm may bereplaced by a high frequency sub-image (or low frequency sub-image) inthe xth layer generated based on a second decomposition algorithm. Insome embodiments, two or more of the transformed images (and/orsub-images) may be used to reconstruct a composite image in thesubsequent procedure.

In 214, a composite image (or sub-image) may be reconstructed based ontwo or more decomposed images (and/or sub-images) generated in 213.Reconstruction block 204 may perform 214. In some embodiments, two ormore sub-images in the same layer may be used to reconstruct an image(or sub-image) in another layer. For example, a first layer including alow frequency sub-image and a high frequency sub-image may be used toreconstruct a composite image. As another example, a second layerincluding a low frequency sub-image and a high frequency sub-image maybe used to reconstruct a low frequency sub-image in a first layer. Insome embodiments, the reconstructed low frequency sub-image in the firstlayer together with a high sub-image in the first layer may be furtherused to reconstruct a composite image. In some embodiments, thereconstructed image may have enhanced information comparing with theinitial image. For example, the image contrast, and/or edge informationmay be enhanced in the reconstructed image. As another example, thenoise information may be reduced or removed in the reconstructed image.In some embodiments, the image (or sub-image) reconstructed in 214 maybe subsequently transmitted to and/or provided to image acquisitionblock 201, decomposition block 202, transformation block 203, storage140, input/output device 150, network 160, or the like, or anycombination thereof. For example, the reconstructed image may bedisplayed in input/output device 150. As another example, thereconstructed image may be stored in the storage 140, or an externaldata storage device via network 160.

FIG. 3-A is a schematic diagram illustrating an exemplary imageacquisition block 201 according to some embodiments of the presentdisclosure. As shown in FIG. 3-A, image acquisition block 201 mayinclude an initial image acquisition unit 301, a region of interest(ROI) extraction unit 302, an ROI edge extraction unit 303, and an ROIimage determination unit 304.

Initial image acquisition unit 301 may acquire an initial image. Theinitial image may include an image acquired from imaging device 110,storage 140, and/or an external data storage device via network 160. Insome embodiments, the initial image may be a processed image generatedfrom decomposition block 202, transformation block 203, and/orreconstruction block 204. In some embodiments, an ROI and/or an ROI edgemay be extracted from the initial image. In some embodiments, theinitial image may be processed to generate an ROI image. In someembodiments, the initial image may be decomposed by decomposition block202 and/or transformed by transformation block 203. For example, abreast CT image may be acquired as an initial image, and the initialimage may be decomposed into one or more layers by decomposition block202. In some embodiments, the initial image may be generated by an FFDMsystem or a DBT system. In some embodiments, the initial image may bedesignated as a positive image, in which the gray levels of the elementsof background may be higher than that of region of interest. In someembodiments, the initial image may be designated as a negative image, inwhich the gray levels of the elements of background may be lower thanthat of region of interest.

Region of interest (ROI) extraction unit 302 may extract an ROI from aninitial image. In some embodiments, the ROI may include a region of animage, the region corresponding to a tissue, an organ, a tumor, or thelike, or any combination thereof. For instance, the ROI may include aregion of an image, the region corresponding to a breast, a region of alung, a region of a skeleton, a region of the liver, a region of thebrain, a region of a kidney, any region of a body, or the like, or anycombination thereof.

For brevity, an image, or a portion thereof (e.g., an ROI in the image)corresponding to an object (e.g., a tissue, an organ, a tumor, etc., ofa subject (e.g., a patient, etc.)) may be referred to as an image, or aportion of thereof (e.g., an ROI) of or including the object, or theobject itself. For instance, an ROI corresponding to the image of aliver may be described as that the ROI includes a liver. As anotherexample, an image of or including a liver may be referred to a liverimage, or simply liver. For brevity, that a portion of an imagecorresponding to an object is processed (e.g., extracted, segmented,etc.) may be described as the object is processed. For instance, that aportion of an image corresponding to a liver is extracted from the restof the image may be described as that the liver is extracted.

In some embodiments, the ROI may include a partial region of a tissue,an organ, a tumor, or the like, or any combination thereof. In someembodiments, the partial region may include a region of the center orclosed to the center of a tissue, organ, tumor, or the like, or anycombination thereof. In some embodiments, the partial region may includea region with elements whose gray levels may be within a determinedrange. In some embodiments, the ROI may be extracted to provide an ROIimage. For example, a region of breast may be extracted to generate abreast image.

ROI edge extraction unit 303 may extract an ROI edge from an initialimage. The ROI edge may refer to an edge of a tissue, an organ, a tumorof interest, etc. For example, the ROI edge may refer to an edge ofbreast, an edge of lung, an edge of skeleton, an edge of liver, an edgeof brain, an edge of kidney, an edge of any region of a body, or thelike, or any combination thereof. In some embodiments, the extracted ROIedge may be used to generate an ROI image.

ROI image determination unit 304 may determine an ROI image. An ROIimage may refer to an image with an ROI and/or an ROI edge that may beextracted from an initial image. ROI image determination unit 304 maydetermine an ROI image based on an ROI extracted by ROI extraction unit302, and/or an ROI edge extracted by ROI edge extraction unit 303. Insome embodiments, the ROI image may include a breast image, a lungimage, a skeleton image, a liver image, a brain image, a kidney image,any part of a body, or the like, or any combination thereof. Forexample, a breast image may be determined, based on a region of breastand an edge of the region of breast. In some embodiments, the ROI imagemay not include the background of the initial image. For example, abreast image may exclude a background of the chest in initial image. Insome embodiments, the ROI image may be decomposed by decomposition block202. For example, a breast image may be decomposed by decompositionblock 202 into a low frequency sub-image and a high frequency sub-image.

FIG. 3-B is a flowchart of an exemplary process for acquiring an imageaccording to some embodiments of the present disclosure. The process mayinclude acquiring initial image 311, extracting ROI 312, extracting ROIedge 313, and determining ROI image 314.

In 311, an initial image may be acquired by initial image acquisitionunit 301. In some embodiments, the initial image may be acquired fromimaging device 110, storage 140, an external data storage device vianetwork 160, or the like, or any combination thereof. In someembodiments, the initial image may be a CT image, an Mill image, a PETimage, an infrared image, or the like, or any combination thereof. Forexample, imaging device 110 may generate a chest CT image, and the chestCT image may be acquired by initial image acquisition unit 301 at 311.In some embodiments, the initial image may be transformed to alog-domain image based on logarithm transformation. The log-domain imagemay be processed in the subsequent operations.

In 312, an ROI may be extracted based on the initial image acquired in311. The operation 312 may be performed by ROI extraction unit 302. Insome embodiments, the ROI may be extracted based on one or moresegmentation algorithms including, for example, OSTU technique,watershed algorithm, threshold segmentation, region growingsegmentation, energy-based 3D reconstruction segmentation, levelset-based segmentation, region split and/or merge segmentation, edgetracing segmentation, statistical pattern recognition, C-meansclustering segmentation, deformable model segmentation, graph searchsegmentation, neural network segmentation, geodesic minimal pathsegmentation, target tracking segmentation, atlas-based segmentation,rule-based segmentation, coupled surface segmentation, model-basedsegmentation, deformable organism segmentation, or the like, or anycombination thereof.

In some embodiments, the ROI may be extracted based on gray levels ofthe initial image. In some embodiments, a gray histogram may begenerated based on the initial image. In some embodiments, the ROI maybe extracted based on the gray histogram. In some embodiments, asegmentation algorithm may be determined according to thecharacteristics of the gray histogram. For example, the gray histogramof an initial image of a breast may have double-peaks, considering theOSTU technique may have relatively high efficiency and precision forgray histogram with double-peaks, the OSTU technique may be used tosegment the initial image. As another example, the watershed algorithmmay be used to extract a region of breast.

In some embodiments, the algorithms for extracting the ROI may be storedin ROI extraction unit 302, storage 140, network 160, or other mobilestorage device. Exemplary mobile storage device may include a flashdrive, a floppy disk, an optical disk, a memory card, a zip disk, amagnetic tape, or the like, or any combination thereof. In someembodiments, the algorithms for extracting the ROI may be retrieved fromone or more external data storage devices via network 160.

In 313, an ROI edge (e.g., a breast edge) may be extracted based on theinitial image acquired in 311. The operation 313 may be performed by ROIedge extraction unit 303. In some embodiments, the ROI edge may beextracted based on the gray level variation characteristics of the edge.In some embodiments, the gray levels of the elements of the ROI edge maybe similar to that of the background. In some embodiments, the ROI edgemay have variations in the gray levels of the elements of the ROI edge.In some embodiments, before extracting the ROI edge, the gray levels ofthe elements of the ROI edge may be enhanced, and thus the ROI edge maybe more distinguishable from the background, and the extraction of ROIedge may be facilitated. In some embodiments, if the initial image is anFFDM image, the initial image may have different noise levels becauseof, e.g., the variation in the X-ray dose. In some embodiments, thenoise level of the initial image may have an impact on the gray levelsof the elements of the ROI edge if the gray levels are enhanced. In someembodiments, in order to reduce or eliminate the impact of the noise, adenoising process may be carried out before the gray levels of theelements of the ROI edge are enhanced. In some embodiments, consideringthe attenuation of X-ray may obey an exponential distribution, in orderto decrease computational complexity and/or increase the efficiency ofROI edge extraction, the ROI edge may be extracted based on a log-domainimage originated from the initial image.

In some embodiments, the log-domain image may be denoised based on adenoising algorithm. The denoising algorithm may include a Gaussianfiltering algorithm, a mean filtering algorithm, a non-local means (NLM)algorithm, a block technique of 3-Dimension (BM3D) algorithm, a totalvariation algorithm, a partial differential equation (PDE) algorithm, awavelet threshold algorithm, or the like, or any combination thereof. Insome embodiments, at 313, the denoised log-domain image may bepre-processed by gray level transformation. In some embodiments, thegray level transformation may refer to a gradient transformation of thegray levels of the elements of the log-domain image. In someembodiments, a Sobel gradient operator may be used in the gradienttransformation. In some embodiments, a difference algorithm may be usedin the gray level transformation. That is, a differential operation ofthe denoised log-domain image may be carried out.

It should be noted that, in some embodiments, if the initial image isdirectly collected from an FFDM system, the gray levels of the elementsof an ROI may be lower than that of the background. That is, the graylevel in different regions may have the relationship: gray levels of theelements of background>gray levels of the elements of ROI edge>graylevels of the elements of ROI. In some embodiments, after denoisingprocess of the log-domain image, the gray level fluctuation may bedecreased (e.g., the gray levels of the elements of the background maytend to be at the same level). In some embodiments, after gray leveltransformation, the gray level in different regions may have therelationship: gray levels of the elements of ROI edge>gray levels of theelements of ROI>gray levels of the elements of background. In someembodiments, with denoising and/or gray level transformation of thelog-domain image, it may be avoided that the elements in the backgroundmay be extracted as elements of the ROI edge, and the accuracy of theROI edge may be improved.

In some embodiments, after denoising process and gray leveltransformation, the ROI edge may be extracted from the log-domain image.In some embodiments, an iteration technique may be used in the ROI edgeextraction. In some embodiments, after denoising process and gray leveltransformation, the log-domain image may be represented byfloating-point data. When using an iteration technique in the ROI edgeextraction, the floating-point data may be used, avoiding the need toconvert the floating-point data to integer data and loss of imageaccuracy or image quality associated with the conversion.

The iteration technique for determining the ROI edge may include a firstiteration, a second iteration, and/or an Nth iteration. An initialthreshold T₀ may be determined as a half of the sum of the maximum graylevel and the minimum gray level in the denoised and/or pre-processedlog-domain image. In the first iteration, the denoised and/orpre-processed log-domain image may be divided as a first region and asecond region based on the threshold T₀. In the first region, the graylevels of elements may be greater than T₀. In the second region, thegray levels of the elements may be equal to and/or less than T₀. In someembodiments, the average gray level of the elements of the first regionand the second region may be calculated separately. For example,G_(first) may represent the average gray level of the elements of thefirst region, and G_(second) may represent the average gray level of theelements of the second region. In some embodiments, T₀ may be updated tohave the value of T₁, in which T₁ may be a half of the sum of G_(first)and G_(second) (i.e., T₁=(G_(first)+G_(second))/2).

In the second iteration, if abs(T₁−T₀)>1 (abs may refer to the absolutevalue), the denoised and/or pre-processed log-domain image may befurther divided as a third region and a fourth region. In the thirdregion, the gray levels of elements may be greater than T₁. In thefourth region, the gray levels of elements may be equal to and/or lessthan T₁. In some embodiments, the average gray level of the elements ofthe third region and the average gray level of the elements of thefourth region may be calculated separately. For example, G_(third) mayrepresent the average gray level of the elements of the third region,and G_(fourth) may represent the average gray level of the elements ofthe fourth region. In some embodiments, T₁ may be updated to have thevalue of T₂, in which T₂ may be a half of the sum of G_(third) andG_(fourth) (i.e., T₂=(G_(third)+G_(fourth))/2).

In the third iteration, if abs(T₂−T₁)>1, the denoised and/orpre-processed log-domain image may be further divided as a fifth regionand a sixth region. In the fifth region, the gray levels of elements maybe greater than T₂. In the sixth region, the gray levels of elements maybe equal to and/or less than T₂. In some embodiments, the average graylevel of the elements of the fifth region and the average gray level ofthe elements of the sixth region may be calculated separately. Forexample, G_(fifth) may represent the average gray level of the elementsof the fifth region, and G_(sixth) may represent the average gray levelof the elements of the sixth region. In some embodiments, T₂ may beupdated to have the value of T₃, in which T₃ may be a half of the sum ofG_(fifth) and G_(sixth) (i.e., T₃=(G_(fifth)+G_(sixth))/2). Until(T_(i+1)−T_(i))<1, the iteration may stop, and the denoised and/orpre-processed log-domain image may not be further divided. T_(i+1) maybe determined as the threshold for ROI edge extraction. The denoisedand/or pre-processed log-domain image may be divided based on T_(i+1). Aset of elements, whose gray levels may be greater than T_(i+1) may bedetermined as belonging to the ROI edge. In some embodiments, thethreshold (e.g., T₀, T₁, T₂, T₃, . . . , T_(i), etc.) in the iterationmay be multiplied by a weighting factor to increase the accuracy of theROI edge. In some embodiments, the weighting factor may be determinedbased on the threshold variation characteristics during the iteration.In some embodiments, the threshold variation characteristics may referto the variation between two or more of the thresholds such as T₀, T₁,T₂, T₃, . . . , T_(i+1), etc. In some embodiments, the weighting factormay be less than 1.

In some embodiments, in addition to iteration technique, the ROI edgemay be extracted using an OTSU algorithm, or any other algorithm thatmay facilitate the extraction of an ROI edge. In some embodiments, theextracted ROI edge may be provided to ROI image determination unit 304at 314. In some embodiments, the extracted ROI edge may be stored in ROIedge extraction unit 303, storage 140, network 160, or other mobilestorage device.

In 314, an ROI image may be determined. The operation 314 may beperformed by ROI image determination unit 304. In some embodiments, theROI image may be determined based on the ROI extracted in 312, and/orthe ROI edge extracted in 313. In some embodiments, the ROI image may begenerated by combining the ROI and the ROI edge. In some embodiments,the generated ROI image may include one or more isolated elements (e.g.,one or more elements of the background may be extracted as elements ofthe ROI image). In some embodiments, the isolated elements may beremoved from the ROI image to enhance the quality of the ROI image. Insome embodiments, the ROI image may be provided to decomposition block202, reconstruction block 204, storage 140, input/output device 150,and/or network 160. For example, a breast image may be decomposed bydecomposition block 202 into L layers.

It should be noted that the above description regarding FIG. 3-A andFIG. 3-B is merely an example, and should not be understood as the onlyembodiment. To those skilled in the art, after understanding the basicprinciples of the operations, the diagram and/or flowchart may bemodified or varied without departing from the principles. Themodifications and variations are still within the scope of the currentapplication described above. For example, 312, 313, and/or 314 may beskipped, which means the initial image may be transmitted todecomposition block 202, transformation block 203, and/or reconstructionblock 204 for further processing.

FIG. 4-A is a flowchart of an exemplary process for decomposing an imageaccording to some embodiments of the present disclosure. The process mayinclude a first decomposition operation 401, and a second decompositionoperation 402.

In 401, an image may be decomposed into L (L may be an integer, and L≧1)layers based on a first decomposition by decomposition block 202. Theimage may include the initial image acquired in 311, the ROI extractedin 312, the ROI edge extracted in 313, the ROI image determined in 314,a decomposed image, a transformed image, an image acquired by imagingdevice 110, storage 140, and/or network 160, or the like, or anycombination thereof. In some embodiments, the image may be decomposed byfirst decomposition unit 202-1, second decomposition unit 202-2, and/orNth decomposition unit 202-N.

In some embodiments, the image may be decomposed using a wavelettransform, a bilateral filtering, a Fourier algorithm, a discrete cosinetransformation, a Laplace transform, any algorithm capable ofdecomposing an image, or the like, or any combination thereof. Forexample, the image may be decomposed into a low frequency sub-image anda high frequency sub-image using a bilateral filter. As another example,the image may be decomposed into L layers by Laplace transform. In someembodiments, a layer may include a low frequency sub-image and a highfrequency sub-image. For example, the image may be decomposed into 3layers by Laplace transform. First, the image may be decomposed into afirst layer including a low frequency sub-image and a high frequencysub-image by Laplace transform; second, the low frequency sub-image inthe first layer may be further decomposed into a second layer includinga low frequency sub-image and a high frequency sub-image by Laplacetransform; third, the low frequency sub-image in the second layer may befurther decomposed into a third layer including a low frequencysub-image and a high frequency sub-image by Laplace transform.

In some embodiments, the image may be decomposed into L layers bywavelet transform. For example, the image may be decomposed into 3layers by wavelet transform. First, the image may be decomposed into afirst layer including a low frequency sub-image and a high frequencysub-image by wavelet transform; second, the low frequency sub-image inthe first layer may be further decomposed into a second layer includinga low frequency sub-image and a high frequency sub-image by wavelettransform; third, the low frequency sub-image in the second layer may befurther decomposed into a third layer including a low frequencysub-image and a high frequency sub-image by wavelet transform.

In 402, an image may be decomposed into L′+N (L′ and N may be integers,L≧1, and N≧1) layers based on a second decomposition by decompositionblock 202. In some embodiments, the image may be decomposed by firstdecomposition unit 202-1, second decomposition unit 202-2, and/or Nthdecomposition unit 202-N. In some embodiments, the image to bedecomposed in 402 may be the same as that to be decomposed in 401. Insome embodiments, the image to be decomposed in 402 may be differentfrom that to be decomposed in 401. In some embodiments, the image to bedecomposed in 402 may be a sub-image generated in 401. In someembodiments, the image may be decomposed by a wavelet transform, abilateral filtering, a Fourier algorithm, a discrete cosinetransformation, a Laplace transform, any algorithm capable ofdecomposing an image, or the like, or any combination thereof.

In some embodiments, the image may be decomposed into L′+N layers by awavelet transform. For example, if L′=3 and N=1, the image may bedecomposed into 4 layers by a wavelet transform. First, the image may bedecomposed into a first layer including a low frequency sub-image and ahigh frequency sub-image by a wavelet transform; second, the lowfrequency sub-image in the first layer may be further decomposed into asecond layer including a low frequency sub-image and a high frequencysub-image by a wavelet transform; third, the low frequency sub-image inthe second layer may be further decomposed into a third layer includinga low frequency sub-image and a high frequency sub-image by a wavelettransform; fourth, the low frequency sub-image in the third layer may befurther decomposed into a fourth layer including a low frequencysub-image and a high frequency sub-image by a wavelet transform.

The sub-image(s) generated in 401 and/or 402 may be provided to imageacquisition block 201, transformation block 203, reconstruction block204, storage 140, input/output device 150, network 160, or the like, orany combination thereof. For example, a sub-image may be transformed bygray level transformation unit 203-1. As another example, a sub-imagemay be transformed by transformation block 203-2. As still anotherexample, one or more of elements of a sub-image may be replaced by thatof another sub-image.

It should be noted that different layers may be decomposed usingdifferent algorithms. In some embodiments, the number (L′+N) of layersgenerated in 402 may be larger than that of layers (L) generated in 401.For example, an image may be decomposed into 4 layers at 402, while thesame image may be decomposed into 3 layers at 401. In some embodiments,401 or 402 may be skipped. In some embodiments, one or more operationsmay be added before or after 401 and/or 402. For example, a thirddecomposition operation may be added after 402.

FIG. 4-B is a schematic diagram of an exemplary L layers decomposed by afirst decomposition according to some embodiments of the presentdisclosure. As shown in FIG. 4-B, the image 411 may be decomposed into Llayers. The image 411 may be decomposed by decomposition block 202. Forexample, the image 411 may be decomposed by first decomposition unit202-1. In some embodiments, the image 411 may be decomposed using one ormore decomposition algorithms. For example, the image may be decomposedusing a Laplace transform. In some embodiments, each layer may includetwo or more sub-images. For example, a single layer may include a lowfrequency sub-image, a medium frequency sub-image, and/or a highfrequency sub-image.

In some embodiments, the image 411 may be decomposed into a first layerincluding a low frequency sub-image 412 and a high frequency sub-image413. The low frequency sub-image 412 in the first layer may bedecomposed into a second layer including a low frequency sub-image 414and a high frequency sub-image 415. The low frequency sub-image 414 inthe second layer may be decomposed into a third layer including a lowfrequency sub-image 416 and a high frequency sub-image 417. Similarly,the low frequency sub-image (not shown) in a (L−1)th layer may bedecomposed into a Lth layer including a low frequency sub-image 418 anda high frequency sub-image 419. In some embodiments, a high frequencysub-image in a layer may be decomposed into a layer including a lowfrequency sub-image and a high frequency sub-image.

FIG. 4-C is a schematic diagram of an exemplary L′+N layers decomposedby a second decomposition according to some embodiments of the presentdisclosure. As shown in FIG. 4-C, the image 421 may be decomposed intoL′+N layers. The image may be decomposed by decomposition block 202. Forexample, the image may be decomposed by second decomposition unit 202-2.In some embodiments, the image may be decomposed using decompositionalgorithms. For example, the image may be decomposed using a wavelettransform.

In some embodiments, the image 421 may be decomposed into a first layerincluding a low frequency sub-image 422 and a high frequency sub-image423. The low frequency sub-image 422 in the first layer may bedecomposed into a second layer including a low frequency sub-image 424and a high frequency sub-image 425. The low frequency sub-image 424 inthe second layer may be decomposed into a third layer including a lowfrequency sub-image 426 and a high frequency sub-image 427. Similarly,the low frequency sub-image (not shown) in a (L′−1)th layer may bedecomposed into a L′th layer including a low frequency sub-image 428 anda high frequency sub-image 429. The low frequency sub-image 428 in L′thlayer may be decomposed into a (L′+1)th layer including a low frequencysub-image 430 and a high frequency sub-image 431. The low frequencysub-image 430 in the (L′+1)th layer may be decomposed into a (L′+2)layer including a low frequency sub-image (not shown) and a highfrequency sub-image (not shown). Similarly, the low frequency sub-image(not shown) in a (L′+N−1)th layer may be decomposed into a (L′+N)thlayer including a low frequency sub-image 432 and a high frequencysub-image 433. In some embodiments, a high frequency sub-image in alayer may be decomposed into a layer including a low frequency sub-imageand a high frequency sub-image.

In some embodiments, L′ may be equal to L. For example, if L′ equals 3,L may be equal to 3. In some embodiments, N may be an integer greaterthan or equal to 1. For example, N may be equal to 1. In someembodiments, each layer may include two or more sub-images. For example,a single layer may include a low frequency sub-image, a medium frequencysub-image, and/or a high frequency sub-image.

FIG. 5-A is a schematic diagram illustrating gray level transformationunit 203-1 according to some embodiments of the present disclosure. Asshown in FIG. 5-A, gray level transformation unit 203-1 may include acharacteristic curve determination unit 501, a characteristic curvedivision unit 502, a transformation curve segment determination unit503, a transformation curve generating unit 504, and a first update unit505.

Characteristic curve determination unit 501 may determine acharacteristic curve. In some embodiments, the characteristic curve maybe determined based on a target image including, for example, an initialimage, an ROI, an ROI edge, an ROI image, or a sub-image. For example,the characteristic curve may be determined based on a low frequencysub-image. The characteristic curve may illustrate the relationshipbetween a distance and a corresponding gray level of an element (asshown in FIG. 6). In some embodiments, the distance may refer to thedistance between an element of the target image and a reference edge ofthe target image. In some embodiments, the reference edge of the targetimage may refer to an ROI edge of the target image, or an outer edge ofthe entire target image. In some embodiments, the distance of an elementof the target image may refer to a minimum distance from the element tothe reference edge. For an element of the target image, the distance toeach element of the reference edge may be calculated. In someembodiments, a line with minimum distance may be determined for each ofa plurality of elements of the target image. In some embodiments, thevalue of the minimum distance may be expressed by the number of elementsin the line with minimum distance. In some embodiments, an element mayhave one or more distances to the reference edge, and the distance inthe characteristic curve may refer to the minimum distance. In someembodiments, different elements may have different distances (e.g., 0.5cm, 0.8 cm, 1 cm, etc.), for example, elements further away from thereference edge may have a larger distance than those close to thereference edge. In some embodiments, a distance may correspond to a graylevel. In some embodiments, one or more elements may have the samedistance, and the elements may have different gray levels; then the graylevel corresponding to the distance may be determined as the averagegray level of the elements with the same distance. For example, threeelements A, B, and C have the same minimum distance of 0.5 cm; then thegray level corresponding to the distance may be calculated asG_(0.5 cm)=(G_(A)+G_(B)+G_(C))/3.

Characteristic curve division unit 502 may divide a characteristic curveinto two or more segments. In some embodiments, characteristic curvedivision unit 502 may generate N curve segments based on thecharacteristic curve, in which N may be an integer larger than 1.

Transformation curve segment determination unit 503 may determine one ormore transformation curve segments. A transformation curve segment maybe part of a transformation curve. In some embodiments, thetransformation curve segment(s) may be determined based on one or morecharacteristic curve segments. For example, a transformation curvesegment may be determined based on a corresponding characteristic curvesegment.

Transformation curve generating unit 504 may generate a transformationcurve based on one or more transformation curve segments. Thetransformation curve may illustrate the relationship between the graylevels of elements in a target image (or sub-image) before gray leveltransformation (e.g., the gray level transformation performed by graylevel transformation unit 203-1) and after gray level transformation(e.g., the gray level transformation performed by gray leveltransformation unit 203-1).

First update unit 505 may update gray level(s) of one or more elementsof the target image based on the transformation curve. In someembodiments, first update unit 505 may transform gray levels of apredetermined range. In some embodiments, first update unit 505 maycompress or amplify the gray levels within the predetermined range. Forexample, first update unit 505 may amplify the gray level of 200 to 300,and thus, the gray level of an element whose original gray level is 200may be changed to 300. As another example, first update unit 505 maydiminish the gray level of 200 to 100, and thus, the gray level of anelement whose original gray level is 200 may be changed to 100.

FIG. 5-B is a flowchart of an exemplary process for transforming graylevels of an image according to some embodiments of the presentdisclosure. As shown in FIG. 5-B, the process may include determining acharacteristic curve 511, dividing a characteristic curve 512,determining transformation curve segments 513, generating atransformation curve 514 based on the determined transformation curvesegments, and updating the gray levels of elements based on thetransformation curve 515.

In 511, a characteristic curve may be determined based on a targetimage. Operation 511 may be performed by characteristic curvedetermination unit 501. In some embodiments, an ROI may be extracted asthe target image. In some embodiments, a high frequency sub-image or alow frequency sub-image may be designated as the target image. Areference edge may be determined in the target image. In someembodiments, an ROI edge may be determined as the reference edge. Insome embodiments, the outer edge of the entire target image may bedetermined as the reference edge. In some embodiments, the referenceedge may be determined automatically, semi-automatically, or manually.For example, a user may artificially define the reference edge through aGUI of the input/output device 150. For an element of the target image,the distance to each element of the reference edge may be calculated. Insome embodiments, a line with minimum distance may be determined foreach of a plurality of elements of the target image. In someembodiments, the value of the minimum distance may be expressed by thenumber of elements in the line with minimum distance. In someembodiments, one or more elements of the target image may have the samevalue of the minimum distance. A gray level corresponding to a minimumdistance D may be the average gray level of the gray levels of theelements having the same or similar value of the minimum distance D. Forinstance, the gray level corresponding to the minimum distance D may bedetermined by dividing the sum of gray levels of the elements having thesame or similar value of the minimum distance D by the number of theseelements. For example, a minimum distance 1.5 cm (or a number ofelements, e.g., 110, 115, 117, 120, etc.) may correspond to element a,element b, and element c. The gray level corresponding to 1.5 cm (or anumber of elements, e.g., 110, 115, 117, 120, etc.) may be calculated bydividing the sum of gray levels of element a, element b, and element cby 3.

In 512, the characteristic curve determined in 511 may be divided into Ncharacteristic curve segments. Operation 512 may be performed bycharacteristic curve division unit 502. In some embodiments, the graylevels of the elements of the reference edge may be similar to that of abackground. In some embodiments, one or more elements of the referenceedge and its neighborhood may have a higher gray level than one or moreelements of the interior region. In order to reduce the difference inthe gray levels of the elements between the reference edge, as well asthe neighborhood of the reference edge, and the interior region, thegray levels of the elements of the reference edge and its neighborhoodmay be diminished. In some embodiments, the reference edge and itsneighborhood may have a lower gray level than the interior region. Inorder to reduce the difference in the gray level between the referenceedge, as well as the neighborhood of the reference edge, and theinterior region, the gray levels of the elements of the reference edgeand its neighborhood may be increased.

In some embodiments, a gray level range may be determined before thecharacteristic curve is divided into characteristic curve segments. Byway of example in which the gray levels of the elements of the referenceedge and its neighborhood is diminished, the maximum value of the graylevel range may be determined as the maximum gray level of the elementsof the reference edge (as illustrated as point A in FIG. 7) before thegray levels of elements of the reference edge are modified. In someembodiments, the minimum value of the gray level range may be determinedbased on individual applications of the technique. In some embodiments,the maximum value of the gray level range and/or the minimum value ofthe gray level range may be adjusted based on a processing efficiency ofthe image processing system 130. In some embodiments, the minimum graylevel may be a gray level corresponding to a predetermined distance inthe characteristic curve. For example, the predetermined distance may be2 cm. If the demand is to transform the gray values of elements that arelocated within a predetermined distance from the reference edge, theminimum value of the gray level range may be determined as thecorresponding gray level of the predetermined distance according to thecharacteristic curve (as illustrated as point P in FIG. 7). In someembodiments, the minimum value of the gray level range may bepredetermined automatically, semi-automatically, or manually. Forexample, a user may define the minimum value through a GUI of theinput/output device 150. As another example, a user may define theminimum value by specifying a distance from the reference edge through aGUI of the input/output device 150.

In some embodiments, after determining the gray level range, the portionof characteristic curve within the gray level range may be divided intoN segments. The number (N) of segments may be determined based on thenumber of gray levels greater than the minimum value of the determinedgray level range. In some embodiments, as the gray level correspondingto a distance may be determined as the average gray level of theelements with the same distance, the gray levels in the characteristiccurve may be discrete data points. That is, the number N may be adefinite number. For example, if the minimum value of the gray levelrange is determined to be 200, the gray levels greater than 200 mayinclude 300, 400, 500, 600, and 700. Thus, the number of gray levelsgreater than 200 is 5, and the characteristic curve may be divided into5 segments, according to the number of gray levels.

In 513, transformation curve segments may be determined based on thedivided N characteristic curve segments generated in 512. Operation 513may be performed by transformation curve segment determination unit 503.In some embodiments, a transformation curve segment may correspond to acharacteristic curve segment. In some embodiments, the transformationcurve segments may be determined one by one. In some embodiments, asecond transformation curve segment may be determined based on apreviously determined first transformation curve segment. In someembodiments, the order for determining different transformation curvesegments may be based on the gray level range of differentcharacteristic curve segments. For example, the characteristic curvesegment with a relatively high gray level range may be first used todetermine its corresponding transformation curve segment, and then thecharacteristic curve segment with a relatively low gray level range maybe used to determine its corresponding transformation curve segment. Asanother example, the characteristic curve segment with a relatively lowgray level range may be first used to determine its correspondingtransformation curve segment, and then the characteristic curve segmentwith a relatively high gray level range may be used to determine itscorresponding transformation curve segment. In some embodiments, theslope of a transformation curve segment may be determined based on thecorresponding characteristic curve segment(s).

FIG. 7 illustrates an exemplary characteristic curve segmented into aplurality of characteristic curve segments. Referring to FIG. 7, thegray levels of the characteristic curve segments (from point A to apredetermined point P) may be within the range of [G_(A), G_(P)], inwhich G_(A) may represent the gray level corresponding to the distanceof point A in the figure, and G_(P) may represent the gray levelcorresponding to the distance of point P in the figure. G_(A) mayrepresent the maximum gray level of the characteristic curve segments,while G_(P) may represent the minimum gray level of the characteristiccurve segments. As illustrated, the characteristic curve may besegmented into three characteristic curve segments, including segmentsAB, BC, and CP. The transformation curve segment corresponding tosegment AB may be first determined, and then segment BC, and segment CP,or vice versa. It should be noted that the number of segments is merelyfor illustration purposes and not intended to limit the scope of thepresent disclosure.

Referring to the characteristic curve segment AB, the slope of atransformation curve segment A′B′ corresponding to characteristic curvesegment AB may be determined based on the gray level of point P in thecharacteristic curve, and the gray levels of point A and point B incharacteristic curve segment AB. For instance, the slope of thetransformation curve segment A′B′ may be determined as the ratio of thegray level of point P in the characteristic curve to an average of thegray levels of point A and point B in characteristic curve segment AB,as illustrated in Equation 1:

$\begin{matrix}{{K_{A^{\prime}B^{\prime}} = \frac{2G_{P}}{G_{A} + G_{B}}},} & (1)\end{matrix}$

wherein K_(A′B′) may be the slop of transformation curve segment A′B′,G_(P) may be the gray level of point P, G_(A) may be the gray level ofpoint A, and G_(B) may be the gray level of point B. The slopes of othertransformation curve segments (e.g., segment B′C′ corresponding tocharacteristic curve segment BC, segment C′P′ corresponding tocharacteristic curve segment CP, etc.) may be calculated in a similarway as segment A′B′. In some embodiments, the starting point of thetransformation curve segment A′B′ may be determined as point A′. In someembodiments, point A′ may have the same gray level as point A in thecharacteristic curve. In some embodiments, the transformation curvesegment A′B′ may be determined based on the slope K_(A′B′) and the graylevel of point A′.

With respect to transformation curve segment B′C′ corresponding tocharacteristic curve segment BC, the slope of segment B′C′ may bedetermined similar to how the slope of segment A′B′ corresponding tocharacteristic curve segment AB is determined. In some embodiments, thestarting point of the transformation curve segment B′C′ may bedetermined as point B′. The gray level of point B′ may be determinedaccording to Equation (2):

$\begin{matrix}{{G_{B^{\prime}} = {G_{A^{\prime}} - {\frac{d_{A} - d_{B}}{d_{A} - d_{P}}\left( {G_{A} - G_{P}} \right)}}},} & (2)\end{matrix}$

wherein G_(B′) may be the gray level of point B′, G_(A′) may be a graylevel of point A′, d_(A) may be the distance of starting point (point A)in the characteristic curve segment AB, d_(B) may be the distance of endpoint (point B) in the characteristic curve segment AB, d_(P) may be thedistance of predetermined point P, G_(A) may be the gray level ofstarting point (point A) of the characteristic curve segment AB, andG_(P) may be the gray level of the predetermined point P. In someembodiments, the transformation curve segment B′C′ may be determinedbased on the slope K_(B′C′) and the gray level of point B′ (i.e.,G_(B′)). Accordingly, each transformation curve segment corresponding toa characteristic curve segment may be determined as described above.

In some embodiments, the slope of Nth transformation curve segment maybe a ratio of the gray level of a predetermined point P in acharacteristic curve to an average gray level of the starting point andend point of Nth characteristic curve segment. The gray level of thestarting point of the Nth transformation curve segment may be the sum ofgray level of the starting point in (N−1)th transformation curve segmentand the gray level variation of the corresponding (N−1)th characteristiccurve segment. The gray level variation of the corresponding (N−1)thcharacteristic curve may refer to the gray level variation of thestarting point and the end point of the (N−1)th characteristic curve.The gray level of the starting point of the Nth transformation curvesegment may be determined according to Equation (2), in which G_(B′) maycorrespond to the gray level of the starting point of the Nthtransformation curve segment, G_(A′) may correspond to the gray level ofthe starting point of the (N−1)th transformation curve segment, d_(A)may correspond to the distance of starting point in the (N−1)thcharacteristic curve segment, d_(B) may correspond to the distance ofend point in the (N−1)th characteristic curve segment, d_(P) maycorrespond to the distance of predetermined point P, G_(A) maycorrespond to the gray level of starting point of the (N−1)thcharacteristic curve segment, and G_(P) may correspond to the gray levelof the predetermined point P.

In some embodiments, in 513, the point with the minimum value(determined in 512) of the gray level range (e.g., predetermined pointP) may be designated as the starting point. In some embodiments, asillustrated in FIG. 7, point P may be designated as the starting pointof segment CP, point C may be designated as the starting point ofsegment BC, and point B may be designated as the starting point ofsegment AB. Accordingly, the starting point for transformation curvesegment C′P′ may be designated as point P′, the starting point fortransformation curve segment B′C′ may be designated as point C′, and thestarting point for transformation curve segment A′B′ may be designatedas point B′. In some embodiments, the gray level of point P′ may be thesame as that of point P, and the gray level for point C′ and B′ may bedetermined based on Equation (2). For example, the gray level of pointC′ may be determined according to Equation (3):

$\begin{matrix}{{G_{C^{\prime}} = {G_{P^{\prime}} + {\frac{d_{P} - d_{C}}{d_{P} - d_{A}}\left( {G_{A} - G_{P}} \right)}}},} & (3)\end{matrix}$

wherein G_(C′) may be the gray level of starting point C′, G_(P′) may bethe gray level of point P′, d_(P) may be the distance of starting pointP in the characteristic curve segment CP, d_(C) may be the distance ofend point C in the characteristic curve segment CP, d_(A) may be thedistance of point A, G_(P) may be the gray level of point P of thecharacteristic curve segment CP, and G_(A) may be the gray level ofpoint A in the characteristic curve segment.

In some embodiments, the slope of transformation curve segment C′P′, andsegment B′C′ may be determined according to Equation (1). Thetransformation curve segment C′P′ corresponding to segment CP may bedetermined based on the gray level of point P′ and the slop of C′P′.Accordingly, the transformation curve segment B′C′ and segment A′B′ maybe so determined.

In 514, a transformation curve may be generated based on thetransformation curve segments determined in 513. Operation 514 may beperformed by transformation curve generating unit 504. In someembodiments, a transformation curve may be generated by curve fittingbased on the determined transformation curve segments. In someembodiments, a transformation curve may be generated using one or morefitting techniques including, for example, a least square technique, aLagrange interpolation technique, a Newton iteration technique, a cubicspline interpolation technique, or the like, or any combination thereof.For example, the transformation curve may be generated by curve fittingbased on a Lagrange interpolation technique. In some embodiments, thetransformation curve may be used for updating gray levels of theelements in the target image.

In 515, gray levels of one or more elements in the target image may beupdated. Operation 515 may be performed by first update unit 505. Insome embodiments, gray levels may be updated based on the transformationcurve generated in 514. In some embodiments, the gray levels of elementswhose gray levels are within the gray level range determined in 512 maybe updated. In some embodiments, according to the transformation curve,the gray levels may be compressed or amplified. In some embodiments, thetransformation curve may be used to compress or amplify the gray levelsof the reference edge in a low frequency sub-image and/or a highfrequency sub-image. In some embodiments, after updating, the graylevels of the elements in the gray level range may be revised(diminished, or increased). In some embodiments, the revision effect maybe linear or nonlinear. In some embodiments, the transformation curvemay be used to revise the gray level of an element whose distance fromthe reference edge is within a predetermined range. In some embodiments,by adjusting a proper gray level range determined in 512, the graylevels of the elements of background in the target image may be notupdated. In some embodiments, after updating, the ROI edge may bedistinguished from the background.

In some embodiments, the gray levels of one or more elements of a targetimage may be compressed based on a transformation curve. Thetransformation curve may be determined based on a positive image, inwhich the gray levels of the elements of background may be higher thanthat of region of interest. In some embodiment, the minimum value of thegray level range of the transformation curve may be the gray level of apredetermined point. The maximum value of the gray level range of thetransformation curve may be a maximum gray levels of the elements of thereference edge. In some embodiments, the number (N) of segments may bedetermined based on the number of gray levels greater than or equal tothe minimum value of the gray level range.

In some embodiments, the gray levels of one or more elements of a targetimage may be amplified based on a transformation curve. Thetransformation curve may be determined based on a negative image, inwhich the gray levels of the elements of background may be lower thanthat of region of interest. After being amplified based on thetransformation curve, the gray levels of one or more elements of thenegative image may be higher than that before transformation. Forexample, the gray levels of one or more elements of a reference edge maybe amplified. The gray levels of elements of the background may not beadjusted. Hence, the contrast between the reference edge and thebackground may be enhanced. The enhanced contrast may facilitate a user(e.g., a doctor) to distinguish the region of interest and thebackground.

By way of example in which the transformation curve is used foramplifying the gray levels of one or more elements of a target image,the gray level of a starting point and a slope of a transformation curvesegment may be determined according to Equation (1), Equation (2),and/or Equation (3). In some embodiments, the minimum value of the graylevel range of the transformation curve may be the minimum gray level ofthe elements of the reference edge. The maximum value of the gray levelrange of the transformation curve may be the gray level of apredetermined point. In some embodiments, the number (N) of segments maybe determined based on the number of gray levels lower than or equal tothe maximum value of the gray level range.

FIG. 6 is a schematic diagram illustrating an exemplary characteristiccurve according to some embodiments of the present disclosure. Asdescribed above, the characteristic curve may express the relationshipbetween the distance and the corresponding gray level. In someembodiments, the distance may be expressed by the number of elementsbetween an element in the target image and the reference edge of thetarget image. The horizontal axis of the characteristic curve mayrepresent minimum distances between a plurality of elements of thetarget image and the reference edge. The minimum distances are shown asthe number of elements. The vertical axis of the characteristic curvemay represent the corresponding average gray level of one or moreelements that may have the same distance. It is understood that thespecific characteristic curve shown in FIG. 6 are for illustrationpurposes, and not intended to limit the scope of the present disclosure.The characteristic curve shown in FIG. 6 may be determined from a lowfrequency sub-image. According to the characteristic curve, the graylevels corresponding to different distances may be determined.

FIG. 7 is a schematic diagram illustrating an exemplary characteristiccurve segmented into a plurality of characteristic curve segmentsaccording to some embodiments of the present disclosure. As shown inFIG. 7, the characteristic curve may be divided into characteristiccurve segments. In some embodiments, the gray levels shown in thevertical axis may be discrete, and the continuous curve is anapproximation merely for illustration purposes. In FIG. 7, a point P maybe predetermined. The gray level range (within which gray levels may betransformed) may be determined as [G_(P), G_(A)], in which G_(P) mayrepresent the lower limit of the gray level range, and G_(A) mayrepresent the upper limit of the gray level range. The characteristiccurve within the range [G_(P), G_(A)] may be divided into 3 segments:segment AB, segment BC, and segment CP. It should be noted that thenumber of segments shown in FIG. 7 is merely for illustration purposesand not intended to limit the scope of the present disclosure.

FIG. 8 is a schematic diagram illustrating an exemplary transformationcurve according to some embodiments of the present disclosure. FIG. 8illustrates the gray level ranges within which gray levels may betransformed. G_(P) may be the minimum value of the gray level range, andG_(P) may correspond to the gray level of the predetermined point Pshown in the FIG. 7. G_(A) may be the maximum value of the gray levelrange, and G_(A) may correspond to the gray level of point A in FIG. 7.The transformation curve P′A′ shown in FIG. 8 may be determined based onthe characteristic curve PA shown in FIG. 7. The transformation curveP′A′ may illustrate a revision effect on gray levels. The transformationcurve exemplified in FIG. 8 indicates that the gray level within [G_(P),G_(A)] may be reduced by way of the transformation.

FIG. 9-A is a schematic diagram illustrating an exemplary weighttransformation unit 203-2 according to some embodiments of the presentdisclosure. The weight transformation unit 203-2 may include a weightimage determination unit 901 and a second update unit 902.

The weight image determination unit 901 may determine a weight image fora target image (or sub-image). The target image (or sub-image) may referto an image (or sub-image) to be processed or being processed by theimage processing system 130. The target image (or sub-image) may includea high frequency sub-image, a low frequency sub-image, a gray levelimage, a color image, or the like. In some embodiments, the weight imagedetermination unit 901 may generate a weight image based on the targetimage (or sub-image). In some embodiments, the weight imagedetermination unit 901 may acquire a weight image from the storage 140,or an external data storage device via the network 160. The weight imagemay refer to a 2D image or 3D image in which the value of each elementmay represent a weighting factor for the corresponding element of thetarget image (or sub-image). In some embodiments, the weight image mayhave the same size as the target image (or sub-image). In someembodiments, the value of the element in a position in the weight imagemay be the weighting factor for an element in a corresponding positionin the target image (or sub-image). For example, the weighting factorfor an element at the position (x, y) or a voxel at the position (x, y,z) of the target image (or sub-image) may be the value of the element atthe position (x, y) or the voxel at the position (x, y, z) of the weightimage.

The second update unit 902 may update the value(s) of one or moreelements of the target image (or sub-image) by the image processingsystem 130. The value of an element may refer to information of theelement including, for example, the gray level, brightness, color, orthe like, or any combination thereof. In some embodiments, the secondupdate unit 902 may update the value(s) of one or more elements based onthe weight image determined by the weight image determination unit 901.In some embodiments, the second update unit 902 and the first updateunit 505 may be integrated into a single update unit that have thefunction of both units.

FIG. 9-B is a flowchart illustrating an exemplary process fortransforming a target image based on a weight image according to someembodiments of the present disclosure. As illustrated herein, anexemplary process for transforming a target image based on a weightimage may include determining a weight image, and updating the targetimage based on the weight image.

In 911, a weight image may be determined based on a target image (orsub-image). The weight image determination 911 may be performed by theweight image determination unit 901. In some embodiments, a weight imagemay be determined based on the gray levels of the elements of the targetimage (or sub-image). In some embodiments, the target image (orsub-image) may include a first class of elements and/or a second classof elements. In some embodiments, the first class of elements may referto noise elements in the target image, while the second class ofelements may refer to other elements in the target image excluding thenoise elements. The noise elements may refer to pixels/voxels with noiseincluding, for example, a Gaussian noise, a salt-and-pepper noise, ashot noise, a quantization noise, a film grain noise, an anisotropicnoise, or the like, or any combination thereof. Other elements may referto pixels/voxels in the target image excluding the noise pixels/voxels.In some embodiments, elements with different gray levels may havedifferent weighting factors. In some embodiments, weighting factors ofelements with different gray levels may be determined by differenttechniques. For example, the first class of elements may have lower graylevels than the second class of elements, and the weighting factors forthe first class of elements may be determined using a differenttechnique than the second class of elements. In some embodiments, weightimage determination unit 901 may determine a gray level range of thefirst class of elements in the target image (or sub-image). In someembodiments, weight image determination unit 901 may adjust thedetermined gray level range of the first class of elements in the targetimage (or sub-image) may. In some embodiments, weight imagedetermination unit 901 may determine a gray level range of the secondclass of elements in the target image (or sub-image). In someembodiments, if the determined gray level range of the first class ofelements is adjusted, the gray level range of the second class ofelements may be adjusted correspondingly. For example, for a targetimage with a gray level range of [G₁, G_(N)] in which the gray levelrange of the first class of elements is determined as [G₁, G_(x)], andthe gray level range of the first class of elements is determined as[G_(x), G_(N)], if the gray level range of the first class of elementsis adjusted to [G₁, G_(x)/2], the gray level range of the first class ofelements may be adjusted to [G_(x)/2, G_(N)] correspondingly. In someembodiments, the gray level range of the first class of elements may bemapped into a first range. The gray level range of the second class ofelements may be mapped into a second range. FIG. 9-C illustrates anexemplary procedure for determining a weight image.

In 912, the target image (or sub-image) may be updated based on theweight image determined in 911. The target image (or sub-image) updatingin 912 may be performed by the second update unit 902. In someembodiments, the target image (or sub-image) may be updated based on theproduct of the gray levels of the elements of the target image (orsub-image) and the corresponding weighting factor of the weight image.In some embodiments, the updating in 912 may be carried out element byelement. For example, the value of an element (x, y) (or (x, y, z)) ofthe target image (or sub-image) may be updated by the product of thevalue itself and the weighting factor of the same element (x, y) (or (x,y, z)) of the weight image.

In some embodiments, a high frequency sub-image of an image layer asillustrated in FIG. 4-B and/or FIG. 4-C may be transformed based on aweight transformation. First, a weight image may be determined based onthe high frequency sub-image in 911. Second, the high frequencysub-image may be updated based on the determined weight image obtainedin 912. In some embodiments, an image reconstruction operation may beadded after 912. For example, the updated high frequency sub-image andthe corresponding low frequency sub-image may be used forreconstruction, and a transformed image (or image layer) may beobtained.

FIG. 9-C is a flowchart illustrating an exemplary process fordetermining a weight image according to some embodiments of the presentdisclosure. In 921, gray level range of the first class of elements in atarget image (or sub-image) may be determined. Operation 921 may beperformed by the weight image determination unit 901. In someembodiments, the target image (or sub-image) may refer to a highfrequency sub-image, a low frequency sub-image, a gray level image, acolor image, or the like. In some embodiments, the gray level range ofthe first class of elements may be determined according to a gray levelthreshold. The gray level threshold may be used for distinguishing thefirst class of elements and the second class of elements. In someembodiments, the gray level threshold may be determined according to adefault setting of the image processing system 130. For example, thegray level threshold may be pre-stored in the storage 140, and theweight image determination unit 901 may retrieve the gray levelthreshold from the storage 140 in 921. In some embodiments, the graylevel threshold may be determined manually or semi-manually. Forexample, a user may determine the gray level threshold through agraphical user interface in the input/output device 150 by inputting orselecting a threshold from a list suggested by the image processingsystem 130.

In some embodiments, the gray level threshold may be determinedaccording to one or more gray levels of the target image (or sub-image).In some embodiments, the gray level threshold may be determined based onthe average gray level of the elements of the entire or a portion of thetarget image, G_(average). For instance, the average gray levelG_(average) may be obtained by dividing the sum of gray levels of allthe elements in the target image by the number of all the elements inthe target image. In some embodiments, the gray level threshold may bedetermined based on a modified average gray level. For example, the graylevel threshold may equal to the average gray level G_(average)multiplied by a pre-determined coefficient k. In some embodiments, thecoefficient k may be determined according to the gray level differencebetween an edge (e.g., an ROI edge, an edge with higher contrast than abackground, an edge of the entire target image, etc.) and the firstclass of elements of the target image. Merely by way of example, thepre-determined coefficient k may be set within a range of [1, 3] (e.g.,k=3). It should be noted that the exemplary range of coefficient k isprovided merely for illustration purposes and not intended to limit thescope of the present disclosure.

In some embodiments, the gray level range of the first class of elementsmay be determined according to the gray level threshold. In someembodiments, in a high frequency sub-image, the first class of elementsmay have gray levels lower than the second class of elements, and anelement whose gray level is lower than the gray level threshold (e.g.,G_(average) itself or G_(average) multiplied by the pre-determinedcoefficient k) may be regarded as the first class of elements (e.g.,noise elements). For example, the elements whose gray levels fall withinthe range of [0, k×G_(average)] may be regarded as belonging to thefirst class.

In 922, the gray level range of the first class of elements in targetimage (or sub-image) may be modified. Operation 922 may be performed bythe weight image determination unit 901. In some embodiments, forexample, in a high frequency sub-image, one or more regions of thetarget image (or sub-image) may have elements with weak details. In someembodiments, the elements with weak details may refer to elements orregions of the target image that have a relatively high noise level. Insome embodiments, the gray level range of the elements with weak detailsmay partially overlap with that of the first class of elements. In someembodiments, the overlapping gray level range may be narrow, and it mayinduce difficulties in determining a modified gray level threshold fordistinguishing the first class of elements and the elements with weakdetails. In some embodiments, the gray level range (e.g., [0,k×G_(average)]) of the first class of elements may be modified byrevising the gray level range (e.g., [0, k×G_(average)]). In someembodiments, revision may refer to stretching or compressing the graylevel range. The revision may include a linear revision, and/or anonlinear revision. In some embodiments, weight image determination unit901 may use one or more functions (e.g., a sine function, a logarithmicfunction, etc.) to nonlinearly revise the gray level range (e.g., [0,k×G_(average)]). In some embodiments, after revision, a revised graylevel range (e.g., a revised [0, k×G_(average)]) may be obtained.

In 923, the gray level range of the first class of elements and/or thatof the second class of elements may be adjusted based on the revisedgray level range obtained in 922. Operation 923 may be performed by theweight image determination unit 901. In some embodiments, a firstthreshold may be determined based on the revised gray level range of thefirst class of elements (e.g., the revised [0, k×G_(average)]). Forexample, the first threshold may be determined as the average gray levelof the first class of elements within the revised gray level range(e.g., the revised [0, k×G_(average)]). The first threshold may beregarded as an updated gray level threshold used for distinguishing thefirst class of elements and the second class of elements. Then the graylevel range of the first class of elements and the gray level range ofthe second class of elements may be adjusted. For instance, the graylevel range of the first class of elements may be [0, the firstthreshold], and the gray level range of the second class of elements maybe (the first threshold, a second threshold]. The second threshold mayrefer to the maximum gray level within the target image (or sub-image).In some embodiments, the elements whose gray levels equal to the firstthreshold may be deemed to belong to the first class. In someembodiments, the elements whose gray levels equal to the first thresholdmay be deemed to belong to the second class. In the followingdescription, it is assumed that the elements whose gray levels equal tothe first threshold may be deemed to belong to the first class. Thisassumption and the corresponding description are for illustrationpurposes and not intended to limit the scope of the present disclosure.

In 924, weighting factors for the first class of elements may bedetermined based on the gray level range (e.g., [0, the firstthreshold]) of the first class of elements adjusted in 923. Operation924 may be performed by the weight image determination unit 901. In someembodiments, the adjusted gray level range [0, the first threshold] maybe mapped into the range of [0, 1]. The mapping process may be performedbased on one or more linear or nonlinear algorithms. A linear mappingprocess may be based on one or more linear algorithms. For example, allthe gray levels within the gray level range [0, the first threshold] maybe divided by the first threshold, and thus the gray level range [0, thefirst threshold] may be mapped into [0, 1]. A nonlinear mapping processmay be based on one or more nonlinear algorithms including, for example,a sine function, a logarithmic function, or the like, or any combinationthereof. In some embodiments, the mapped range [0, 1] may be defined asthe weighting factors for the first class of elements. That is, a graylevel G_(x) (G_(x)ε[0, the first threshold]) may be transformed asG_(x)′ (G_(x)′ε[0, 1]) after mapping, and the value G_(x)′ may bedefined as the weighting factors for the first class of elements with agray level G_(x).

In 925, weighting factors for the second class of elements may bedetermined based on the gray level range (e.g., (the first threshold,the second threshold]) of the second class of elements adjusted in 923.Operation 925 may be performed by the weight image determination unit901. In some embodiments, the adjusted gray level range (the firstthreshold, the second threshold] may be mapped into the range of (1, G].The mapping process may be performed based on one or more linear ornonlinear algorithms. A linear mapping process may be based on one ormore linear algorithms. A nonlinear mapping process may be based on oneor more nonlinear algorithms including, for example, a sine function, alogarithmic function, or the like, or any combination thereof. In someembodiments, the mapped range (1, G] may be defined as the weightingfactors for the second class of elements. That is, a gray level G_(y)(G_(y)ε(the first threshold, the second threshold]) may be transformedas G_(y)′ (G_(y)′ε(1, G]) after mapping, and the value G_(y)′ may bedefined as the weighting factors for the second class of elements with agray level G_(y). In some embodiments, the value of G may be determinedor selected according to a desired image enhancement effect. Forexample, G may be determined as 2, 3, etc. It should be noted that thevalue of G is merely for illustration purposes and not intended to limitthe scope of the present disclosure.

In 926, the gray levels of the elements within the adjusted gray levelrange of the first class of elements obtained in 923 may be modified.Operation 926 may be performed by the weight image determination unit901. In some embodiments, gray levels of the first class of elements maybe modified based on corresponding weighting factors defined in 924.Merely by way of example, the gray levels of the first class of elementsmay be replaced by the corresponding weighting factors.

In 927, the gray levels of the elements within the adjusted gray levelrange of the second class of elements obtained in 923 may be modified.Operation 927 may be performed by the weight image determination unit901. In some embodiments, gray levels of the second class of elementsmay be modified based on corresponding weighting factors defined in 925.Merely by way of example, the gray levels of the second class ofelements may be replaced by the corresponding weighting factors.

In 928, a weight image may be generated. Operation 928 may be performedby the weight image determination unit 901. In some embodiments, weightimage may be generated based on gray levels of the first class ofelements modified in 926, and gray levels of the second class ofelements modified in 927. Merely by way of example, after modificationprocess in 926 and 927, the target image (or sub-image) may become aweight image spontaneously. In some embodiments, 926 and 927 may beomitted, and a weight image may be generated based on the weightingfactors of the first class of elements and the second class of elementsand the respective positions of these weighting factors. For example, afirst class of element may have a position (m, n) in the target image(or sub-image), and an adjusted gray level G_(x) (G_(x)ε[0, the firstthreshold]), then a weighting factor G_(x)′ (G_(x)′ε[0, 1]) may be givento the element at the same position (m, n) in the weight image. Asanother example, another element may have a position (m′, n′) in thetarget image (or sub-image), and an adjusted gray level G_(y) (G_(y)E(the first threshold, the second threshold]), then a weighting factorG_(y)′ (G_(y)′ε(1, G]) may be given to the element at the same position(m′, n′) in the weight image.

It should be noted that the above description about the weighttransformation unit 203-2 and relevant flowchart is merely an example,and should not be understood as the only embodiment. To those skilled inthe art, after understanding the basic principles of the connectionbetween different units/operations, the units/operations and connectionbetween the units/operations may be modified or varied without departingfrom the principles. The modifications and variations are still withinthe scope of the current application described above. For example, the924 and 925 may be performed simultaneously, or integrated into oneoperation. As another example, 926 and 927 may be omitted. As stillanother example, 922 and 923 may be integrated into one operation. As afurther example, 924 and 926 may be integrated into one operation. Asstill a further example, 925 and 927 may be integrated into oneoperation.

FIG. 10 is a schematic diagram illustrating an exemplary process forgenerating a weight image. A target image 1001 (e.g., a high frequencysub-image) may have a plurality of the first class of elements 1002 asillustrated by the hollow circle(s) in region 1010. Region 1010 mayrefer to the region of the first class of elements 1002 within the graylevel range determined in 921. The region excluding region 1010 mayrefer to the region of the second class of elements. See region 1020within 1001 but outside of region 1010. Suppose the gray level range ofthe first class of elements 1002 in region 1010 is [0, 100], after anonlinear revision process as described in 922, the gray level range ofthe first class of elements 1002 in region 1010 may be revise to be [0,200]. Suppose the average gray level of the first class of elements 1002in region 1010 is 150, designated as the first threshold, after anadjustment process in 923, the gray level range of the first class ofelements 1002 may be adjusted as [0, 150], and the gray level range ofthe second class of elements may be adjusted as (150, the secondthreshold]. The first class of elements 1005 in region 1040 whose graylevels are within the range [0, 150] may be regarded as real first classof elements. The gray level range [0, 150] may be mapped into [0, 1] in924. For example, all the gray levels within the range [0, 150] may bedivided by 150. The values within the mapped range [0, 1] may representthe weighting factors of the first class of elements 1005 in region1040. The gray level range (150, the second threshold] may be mappedinto (1, G] in 925. The values within the mapped range (1, G] mayrepresent the weighting factors of the second class of elements of thetarget image 1001 excluding region 1040. In some embodiments, the valueof G may be determined based on the second threshold. In someembodiments, the second threshold may refer to the maximum gray levelwithin the target image 1001. In some embodiments, the value of G may bedetermined or selected according to a desired image enhancement effect.For example, G may be determined as 2, 3, etc. It should be noted thatthe value of G is merely for illustration purposes and not intended tolimit the scope of the present disclosure. The values within the mappedrange (1, G] may represent the weighting factors of the second class ofelements excluding the first class of elements 1005 in region 1040. Byreplacing the gray levels of the first class of elements 1005 in region1040 with the corresponding weighting factors in the range [0, 1], andreplacing gray levels of the second class of elements excluding thefirst class of elements 1005 in region 1040 with the correspondingweighting factors in the range (1, G], a weight image corresponding tothe target image 1001 may be obtained. The weight image may illustratethe weighting factor of each element in the target image 1001.

FIG. 11-A is a flowchart of an exemplary process for reconstructing acomposite image based on one layer of a target image according to someembodiments of the present disclosure. The reconstruction process mayinclude acquiring sub-image(s) 1101, and reconstructing composite image1102.

In 1101, a low frequency sub-image and a high frequency sub-image of atarget image may be acquired. Operation 1101 may be performed byreconstruction block 204. In some embodiments, the low frequencysub-image and/or high frequency sub-image may be generated bydecomposition block 202. In some embodiments, the low frequencysub-image and high frequency sub-image may be generated using the samedecomposition algorithm. In some embodiments, the low frequencysub-image and high frequency sub-image may be generated using differentdecomposition algorithms. For instance, the low frequency sub-image maybe generated using a first decomposition algorithm, and the highfrequency sub-image may be generated using a second decompositionalgorithm. For example, the low frequency sub-image may be generatedusing Laplace transform, while the high frequency sub-image may begenerated using wavelet transform.

In some embodiments, the low frequency sub-image and the high frequencysub-image may be generated from the same target image (or sub-image). Insome embodiments, the low frequency sub-image and the high frequencysub-image may be generated from the same layer. In some embodiments, thelow frequency sub-image and the high frequency sub-image may begenerated from different target images (or sub-images) or differentlayers of the same target image.

In some embodiments, the low frequency sub-image may have beentransformed by transformation block 203. In some embodiments, the highfrequency sub-image may have been transformed by transformation block203. For example, the low frequency sub-image and/or the high frequencysub-image may have been transformed based on gray level transformationas illustrated in FIG. 5-B. As another example, the low frequencysub-image and/or the high frequency sub-image may have been transformedbased on weight transformation as illustrated in FIG. 9-C. As stillanother example, the low frequency sub-image and/or the high frequencysub-image may have been enhanced linearly or nonlinearly as illustratedin FIG. 2-A and FIG. 2-B. As a further example, the low frequencysub-image and/or the high frequency sub-image may have been denoised. Asstill a further example, the low frequency sub-image and/or the highfrequency sub-image may have been transformed through one or moreinterpolation processes (e.g., the sub-image may have been up-sampled bythe up-sampling unit 203-4). In some embodiments, the low frequencysub-image and/or the high frequency sub-image may have been transformedthrough one or more transformation processes illustrated in the presentdisclosure.

In some embodiments, the (transformed) low frequency sub-image and the(transformed) high frequency sub-image may be acquired from thedecomposition block 202, the transformation block 203, the storage 140,the input/output device 150, or an external data storage device vianetwork 160.

In 1102, a composite image may be reconstructed based on the(transformed) low frequency sub-image and the (transformed) highfrequency sub-image acquired in 1101. Operation 1102 may be performed byreconstruction block 204. In some embodiments, the composite imagereconstructed may correspond to a target image (e.g., an initial image,an ROI of the initial image, an ROI image, any image generated in imageprocessing procedure, or the like, or any combination thereof). In someembodiments, the composite image may be an enhanced target image, acompressed target image, a transformed target image, or the like, or anycombination thereof. For example, in comparison with the target image,the composite image may have improved contrast, enhanced details, moredistinct edge, or the like, or any combination thereof.

In some embodiments, 1102 may by performed based on one or morereconstruction algorithms. The reconstruction algorithms may include ananalytic reconstruction algorithm, an iterative reconstructionalgorithm, or the like, or any combination thereof. The analyticreconstruction algorithm may include a filtered back projection (FBP)algorithm, a back projection filtration (BFP) algorithm, a p-filteredlayergram, or the like. The iterative reconstruction algorithm mayinclude an ordered subset expectation maximization (OSEM) algorithm, amaximum likelihood expectation maximization (MLEM) algorithm, etc.

In some embodiments, the composite image may be reconstructed bysuperimposing the (transformed) low frequency sub-image and the(transformed) high frequency sub-image. For example, the gray levels ofelements in the (transformed) low frequency sub-image may be added withthat of the same elements in the (transformed) high frequency sub-image,and the composite image may be obtained.

In some embodiments, the composite image may be reconstructed based on areconstruction algorithm corresponding to a decomposition algorithm. Forexample, if the (transformed) low frequency sub-image and the(transformed) high frequency sub-image are generated using wavelettransform, the composite image may be reconstructed using an inversewavelet transform. In some embodiments, the composite image may befurther used in a subsequent reconstruction process. For example, thecomposite image generated in 1102 together with a sub-image may befurther used for reconstructing a new composite image. In someembodiments, a composite image may be reconstructed based on two or morelayers, as shown in FIG. 11-B and FIG. 11-C. In some embodiments, thecomposite image may be transmitted to image acquisition block 201,decomposition block 202, transformation block 203, storage 140, network160, or the like, or any combination thereof.

FIG. 11-B is a flowchart of an exemplary process for reconstructing alow frequency sub-image of L′th layer generated from the seconddecomposition according to some embodiments of the present disclosure.The process may include enhancing sub-image 1111, and reconstructingsub-image 1112. It is understood that the process illustrated in FIG.11-B may be applicable for reconstructing a low frequency sub-image ofL′th layer generated from the first decomposition according to someembodiments of the present disclosure.

In 1111, the high frequency sub-images from (L′+1)th layer to (L′+N)thlayer generated from a second decomposition may be enhanced. In someembodiments, the high frequency sub-images may be enhanced based on oneor more enhancement techniques. The enhancement techniques may includefiltering with a morphological operator, histogram equalization, noiseremoval using, e.g., Wiener filter techniques, linear or nonlinearcontrast adjustment, median filtering, unsharp mask filtering,contrast-limited adaptive histogram equalization (CLAHE), decorrelationstretch, or the like, or any combination thereof. In some embodiments,the enhancement technique may refer to linear/nonlinear enhancement. Insome embodiments, the linear enhancement may include a Max-Min contrasttechnique, a percentage contrast technique, a piecewise contrasttechnique, or the like, or any combination thereof. The nonlinearenhancement may include histogram equalization, adaptive histogramequalization, a homomorphic filter technique, an unsharp mask, or thelike, or any combination thereof. In some embodiments, the enhancementtechnique may include gray level transformation (as shown in FIG. 5-B),weight transformation (FIG. 9-C), or the like, or any combinationthereof. In some embodiments, the high frequency sub-images from(L′+1)th layer to (L′+N)th layer generated from the second decompositionmay be enhanced by linear/nonlinear enhancement. For example, the highfrequency sub-images of the (L′+1)th layer generated from a seconddecomposition may be enhanced by the Max-Min contrast technique. In someembodiments, the high frequency sub-images from (L′+1)th layer to(L′+N)th layer may be enhanced using different techniques.

In 1112, the low frequency sub-image of L′th layer generated from thesecond decomposition may be reconstructed. In some embodiments, the lowfrequency sub-image of L′th layer generated from the seconddecomposition may be reconstructed based on the high frequency sub-imagefrom (L′+1)th layer to (L′+N)th layer enhanced in 1111. In someembodiments, the enhanced high frequency sub-image of (L′+N)th layer andthe low frequency sub-image of (L′+N)th layer may be used to reconstructthe low frequency sub-image of (L′+N−1)th layer. The reconstructed lowfrequency sub-image of (L′+N−1)th layer and the enhanced high frequencysub-image of (L′+N−1)th layer may be used to reconstruct the lowfrequency sub-image of (L′+N−2)th layer. Accordingly, the reconstructedlow frequency sub-image of (L′+1)th layer and the enhanced highfrequency sub-image of (L′+1)th layer may be used to reconstruct the lowfrequency sub-image of L′th layer. For example, if L′ equals to 3 and Nequals to 2, the low frequency sub-image of the fourth layer may bereconstructed based on the low frequency sub-image of the fifth layerand the enhanced high frequency sub-image of the fifth layer; and thenthe low frequency sub-image of the third layer may be reconstructedbased on the reconstructed low frequency sub-image of the fourth layerand the enhanced high frequency sub-image of the fourth layer. In someembodiments, the low frequency sub-image of the L′th layer may befurther used to reconstruct a composite image by reconstruction block204. FIG. 11-C illustrates an exemplary procedure. It should be notedthat in some embodiments, in 1111 and/or 1112, the low frequencysub-image from (L′+1)th layer to (L′+N)th layer may be enhanced beforeand/or after reconstruction.

FIG. 11-C is a flowchart of an exemplary process for reconstructing acomposite image based on L layers generated from the first decompositionaccording to some embodiments of the present disclosure. The process mayinclude 1121 for updating sub-image, one or more operations (e.g., 1122,1123, etc.) for reconstructing sub-image, and 1124 for reconstructingthe composite image. In some embodiments, the first decomposition andcorresponding reconstruction may improve detail information of a targetimage. In some embodiments, the second decomposition and correspondingreconstruction may improve edge information of the target image.Reconstruction using the sub-images of the first decomposition and thesub-images of the second decomposition may correspondingly improvedetail information and edge information of the target image, and/orenhance the contrast of the target image. Besides, the artifacts of thetarget image may be reduced by transforming the sub-images of the firstdecomposition and/or the second decomposition.

In 1121, the low frequency sub-image of Lth layer generated from thefirst decomposition may be updated. In some embodiments, the lowfrequency sub-image of Lth layer may be updated based on the lowfrequency sub-image of L′th layer generated from the seconddecomposition (as shown in FIG. 11-B). In some embodiments, the lowfrequency sub-image of Lth layer generated from the first decompositionmay be replaced by the (transformed) low frequency sub-image of L′thlayer generated from the second decomposition. In some embodiments, Lmay be equal to L′. In some embodiments, the sub-images generated fromthe first decomposition and the sub-images generated from the seconddecomposition may be derived from the same target image. In someembodiments, the low frequency sub-image of Lth layer may be updatedthrough transformation. In some embodiments, the updated low frequencysub-image of Lth layer may be further transformed. Transformationtechniques may include gray level transformation, weight transformation,linear/nonlinear enhancement, up-sampling, or the like, or anycombination thereof. For example, the updated low frequency sub-image ofLth layer may be up-sampled using bilinear interpolation. In someembodiments, the updated low frequency sub-image of the Lth layer may beused to reconstruct a low frequency sub-image of (L−1)th layer in 1122.

In 1122, the low frequency sub-image of (L−1)th layer generated from thefirst decomposition may be updated. In some embodiments, the lowfrequency sub-image of (L−1)th layer may be reconstructed based on theupdated low frequency sub-image of Lth layer and the high frequencysub-image of Lth layer generated from the first decomposition. In someembodiments, the updated low frequency sub-image of Lth layer may beup-sampled by up-sampling unit 203-4. In some embodiments, the updatedlow frequency sub-image of (L−1)th layer may be further transformed. Forexample, the low frequency sub-image of (L−1)th layer may be up-sampledby up-sampling unit 203-4.

In 1123, the low frequency sub-image of (L−2)th layer generated from thefirst decomposition may be updated. In some embodiments, the lowfrequency sub-image of (L−2)th layer may be reconstructed based on theupdated low frequency sub-image of (L−1)th layer and the high frequencysub-image of (L−1)th layer generated from the first decomposition. Insome embodiments, the updated low frequency sub-image of (L−2)th layermay be further transformed. For example, the low frequency sub-image of(L−2)th layer may be up-sampled by up-sampling unit 203-4.

Similarly, the low frequency sub-image of first layer generated from thefirst decomposition may be updated. In some embodiments, the updated lowfrequency sub-image of first layer may be further transformed. In 1124,a composite image may be reconstructed based on the updated lowfrequency sub-image of first layer and the high frequency sub-image offirst layer generated from the first decomposition.

It should be noted that one or more operations for updating lowfrequency sub-images of different layers generated from the firstdecomposition maybe added between 1123 and 1124. In some embodiments,one or more high frequency sub-images of first layer through Lth layermay be transformed before or after an updating operation.

For illustration purposes, an exemplary process may be described below.It should be noted that the description below is merely an example, andshould not be understood as the only embodiment. To those skilled in theart, after understanding the basic principles of the operations,flowchart may be modified or varied without departing from theprinciples. The modifications and variations are still within the scopeof the current application described above.

In some embodiments, a target image may be decomposed by Laplacetransform into 3 layers, and the target image may be decomposed bywavelet transform into 5 layers. The low frequency sub-image of thethird layer generated by Laplace transform may be updated by the lowfrequency sub-image of third layer generated by wavelet transform.

Merely by way of example, the low frequency sub-image of the fifth layergenerated by wavelet transform may be transformed using bilinearinterpolation; the high frequency sub-image of the fifth layer generatedby wavelet transform may be transformed using nonlinear enhancement. Thelow frequency sub-image of the fourth layer may be reconstructed basedon the transformed low frequency sub-image of the fifth layer and thetransformed high frequency sub-image of the fifth layer. Thereconstructed low frequency sub-image of the fourth layer may betransformed using bilinear interpolation. The high frequency sub-imageof the fourth layer generated by wavelet transform may be transformedusing nonlinear enhancement. The low frequency sub-image of the thirdlayer may be reconstructed based on the transformed low frequencysub-image of the fourth layer and the transformed high frequencysub-image of the fourth layer.

The low frequency sub-image of the third layer generated from Laplacetransform may be replaced by the reconstructed low frequency sub-imageof the third layer generated by wavelet transform. The updated lowfrequency sub-image of third layer generated by Laplace transform may befurther used to update the sub-images of the second layer and the firstlayer generated by Laplace transform.

Merely by way of example, the updated low frequency sub-image of thirdlayer generated by Laplace transform may be transformed using bilinearinterpolation; the high frequency sub-image of third layer generated byLaplace transform may be transformed using nonlinear enhancement. Thetransformed low frequency sub-image of the third layer and thetransformed high frequency sub-image of the third layer may be used toreconstruct a low frequency sub-image of the second layer. The updatedlow frequency sub-image of the second layer may be transformed usingbilinear interpolation. The high frequency sub-image of the second layergenerated by Laplace transform may be transformed using nonlinearenhancement. The transformed low frequency sub-image of the second layerand the transformed high frequency sub-image of the second layer may beused to reconstruct a low frequency sub-image of the first layer. Theupdated low frequency sub-image of the first layer may be transformedusing bilinear interpolation. The high frequency sub-image of the firstlayer generated by Laplace transform may be transformed using nonlinearenhancement. The transformed low frequency sub-image of the first layerand the transformed high frequency sub-image of the first layer may beused to reconstruct a composite image.

In some embodiments, information in the L layers generated by Laplacetransform and information in L′+N layers generated by wavelet transformmay be combined. The low frequency sub-image of Lth layer generated bywavelet transform may be reconstructed based on low frequency sub-imagesin subsequent layers and enhanced high frequency sub-images insubsequent layers. The updated low frequency sub-image of the Lth layergenerated by Laplace transform and enhanced high frequency sub-image ofthe first layer through Lth layer may be used to reconstruct an enhancedimage. Therefore, details and/or the edges in the target image may beenhanced.

In some embodiments, different interpolation algorithms may be used inthe reconstruction of low frequency sub-images of different layersgenerated by wavelet transform and/or Laplace transform. As a result,the image contrast may be enhanced, and artifacts may be removed fromthe enhanced image. Hence, the image quality may be improved.

In some embodiments, one or more high frequency sub-images may beupdated based on weight transformation. In some embodiments, one or morehigh frequency sub-images may be enhanced by enhancement unit 203-3,and/or denoised based on a denoising algorithm. For example, a highfrequency sub-image of a layer may be denoised using Gaussian filtering.A denoised high frequency sub-image may be used in reconstructing a lowfrequency sub-image.

It should be noted that the above description regarding imageacquisition, image decomposition, image transformation, and imagereconstruction is merely for illustration purposes. To those skilled inthe art, after understanding the basic principles of the operations,processing procedure may be modified, combined or varied withoutdeparting from the principles. The modifications, combinations andvariations are still within the scope of the current applicationdescribed above. An exemplary process are described below.

First, a breast image may be generated from a CT image for a chest. Asillustrated in FIG. 3-B, a region of breast may be extracted from the CTimage, a breast edge may be extracted from the CT image, and a breastimage may be generated based on the region of breast and the breastedge.

Second, the breast image may be decomposed into a low frequencysub-image and a high frequency sub-image. The low frequency sub-imagemay include information regarding the breast edge.

Third, the low frequency sub-image of the breast image may betransformed using gray level transformation. In some embodiments, duringa procedure for generating the CT image for a chest, the breast may bepressed. The thickness of the pressed breast may be non-uniform, and thegray level in the breast image may be uneven. For example, thebrightness of elements of a breast edge may be darker than that in aregion adjacent to the breast edge. The gray levels of the elements ofthe breast edge may be close to that of the background. Using the graylevel transformation technique illustrated in FIG. 5-B, the imagequality of the breast image may be improved. As illustrated in FIG. 5-B,a characteristic curve may be determined based on the low frequencysub-image. In some embodiments, the characteristic curve may be dividedinto N segments. In some embodiments, transformation curve segments maybe determined based on the divided N characteristic curve segments. Thena transformation curve may be generated based on the transformationcurve segments. In some embodiments, gray levels of elements in apredetermined region of the low frequency sub-image may be updated basedon the transformation curve. The predetermined region may refer to theregion within which the distance of elements may be within apredetermined value. The gray levels of in the predetermined region maybe close to that in the neighborhood region.

Finally, the updated low frequency sub-image and the high frequencysub-image may be used to reconstruct a composite breast image. The graylevels of elements in the composite breast image may be evened, and thethickness of the breast may be well-proportioned.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. An image processing method implemented on at least one machine eachof which has at least one processor and storage, the method comprising:acquiring a target image, the target image including a plurality ofelements, an element corresponding to a pixel or a voxel; decomposingthe target image into at least one layer, the at least one layerincluding a low frequency sub-image and a high frequency sub-image;transforming the at least one layer; and reconstructing the transformedlayer into a composite image.
 2. The method of claim 1, the acquiring atarget image comprising: acquiring an initial image; extracting, basedon the initial image, a region of interest (ROI); extracting, based onthe initial image, an ROI edge; and determining, based on the ROI andthe ROI edge, an ROI image as the target image.
 3. (canceled)
 4. Themethod of claim 2, the extracting an ROI edge comprising: denoising theinitial image; pre-processing, based on a gradient transform, thedenoised initial image; and detecting, based on an OTSU algorithm or aniterative algorithm, the ROI edge. 5-7. (canceled)
 8. The method ofclaim 1, the low frequency sub-image including a predetermined regionincluding a plurality of gray levels, and the transforming the at leastone layer comprising: determining a reference edge in the low frequencysub-image; determining, based on the low frequency sub-image, acharacteristic curve, the characteristic curve illustrating therelationship between a distance and a gray level corresponding to thedistance, wherein the distance is a distance between a first element inthe low frequency sub-image and a second element in the reference edge,and the first element corresponds to the second element, wherein thegray level is determined based on the plurality of gray levels;determining, based on the characteristic curve, a transformation curve,the transformation curve illustrating the relationship between the graylevel before transformation and the gray level after transformation; andupdating, based on the transformation curve, the plurality of graylevels of the predetermined region.
 9. (canceled)
 10. The method ofclaim 8, the determining a transformation curve comprising: dividing thecharacteristic curve into N characteristic curve segments; determining,based on the N characteristic curve segments, N transformation curvesegments, wherein a characteristic curve segment corresponds to atransformation curve segment; and generating, based on the Ntransformation curve segments, the transformation curve.
 11. The methodof claim 10, the determining N transformation curve segments comprising:for an xth transformation curve segment of the N transformation curvesegments, calculating a slope of the xth transformation curve segmentbased on the gray level of a predetermined point in the characteristiccurve, a gray level of the starting point of an xth characteristic curvesegment, and a gray level of the end point of the xth characteristiccurve segment, the xth characteristic curve segment corresponding to thexth transformation curve segment, wherein x is an integer, 1≦x≦N;determining a gray level of the starting point in the xth transformationcurve segment, comprising: if x=1, designating the gray level of thestarting point in the xth characteristic curve segment as the gray levelof the starting point in the xth transformation curve segment; and if1<x≦N, determining the gray level of the starting point in the xthtransformation curve segment based on the gray level of the startingpoint of the (x−1)th transformation curve segment and a gray levelvariation of the (x−1)th characteristic curve segment.
 12. The method ofclaim 10 further comprising: determining a gray level range of thecharacteristic curve, wherein the gray level range is a range withinwhich at least one gray level is to be transformed, and the gray levelrange corresponds to a portion of the characteristic curve; anddesignating the maximum value or minimum value of the gray level rangeas the gray level of the predetermined point in the characteristiccurve.
 13. The method of claim 1, the decomposing the target imagecomprising: decomposing, based on a first decomposition, the targetimage into L layers, each layer of the L layers including a lowfrequency sub-image and a high frequency sub-image, L≧1; anddecomposing, based on a second decomposition, the target image into L′+Nimage layers, each layer of the L′+N layers including a low frequencysub-image and a high frequency sub-image, L′≧1, and N≧1. 14-15.(canceled)
 16. The method of claim 13, the reconstructing thetransformed layer comprising: updating, based on the low frequencysub-image of the L′th layer generated from the second decomposition, thelow frequency sub-image of the Lth layer generated from the firstdecomposition; and reconstructing, based on the high frequencysub-images of the L layers generated by the first decomposition and theupdated low frequency sub-image of the Lth layer, the composite image.17. The method of claim 16 further comprising: enhancing the highfrequency sub-images of the L layers generated by the firstdecomposition.
 18. The method of claim 16, the reconstructing thecomposite image comprising: for each of a plurality of iterationsup-sampling the low frequency sub-image of the (L−i)th layer; updating,based on the up-sampled low frequency sub-image of the (L−i)th layer andthe high frequency sub-image of the (L−i)th layer, the low frequencysub-image of the (L−i−1)th layer, 0≦i≦L−1; and reconstructing, based onthe updated low frequency sub-image of the first layer and the highfrequency sub-image of the first layer, the composite image. 19.(canceled)
 20. The method of claim 16 further comprising: updating thelow frequency sub-image of the L′th layer generated by the seconddecomposition based on the low frequency sub-image of the (L′+N)th layergenerated by the second decomposition and the high frequency sub-imagesof the (L′+1)th layer through the (L′+N)th layer generated from thesecond decomposition.
 21. The method of claim 1, the high frequencysub-image including a plurality of elements, and the transforming the atleast one layer comprising: generating a weight image for the highfrequency sub-image, the weight image including a plurality of weightscorresponding to the plurality of elements; and updating, based on theweight image, the high frequency sub-image.
 22. The method of claim 21,the high frequency sub-image including a first class of elements and asecond class of elements, and the generating the weight imagecomprising: determining a gray level range of the first class ofelements in the high frequency sub-image; determining, based on the graylevel range of the first class of elements, a gray level range of thesecond class of elements in the high frequency sub-image; mapping thegray level range of the first class of elements into [0, 1];determining, based on the mapped gray level range of the first class ofelements, weighting factors for the first class of elements; mapping thegray level range of the second class of elements into (1, G], wherein Gis a predetermined value; determining, based on the mapped gray levelrange of the second class of elements, weighting factors for the secondclass of elements; and generating, based on the weighting factors forthe first class of elements and the weighting factors for the secondclass of elements, the weight image.
 23. The method of claim 22, thedetermining a gray level range of the first class of elementscomprising: determining, based on a gray level threshold, an initialgray level range of the first class of elements; modifying the initialgray level range of the first class of elements; and adjusting, based onthe modified gray level range of the first class of elements, theinitial gray level range of the first class of elements.
 24. The methodof claim 23, the adjusting the initial gray level range of the firstclass of elements comprising: calculating, based on the modified graylevel range of the first class of elements, a first threshold; anddetermining the gray level range of the first class of elements as [0,the first threshold].
 25. The method of claim 21, the transforming thehigh frequency sub-image comprising: multiplying the gray levels of thehigh frequency sub-image by that of the weight image.
 26. The method ofclaim 1, the transforming the at least one layer comprising:transforming the high frequency sub-image or the low frequency sub-imageby at least one of linear/nonlinear enhancement or denoising. 27.(canceled)
 28. A non-transitory computer readable medium comprisingexecutable instructions that, when executed by at least one processor,cause the at least one processor to effectuate a method comprising:acquiring a target image, the target image including a plurality ofelements, an element corresponding to a pixel or a voxel; decomposingthe target image into at least one layer, the at least one layerincluding a low frequency sub-image and a high frequency sub-image;transforming the at least one layer; and reconstructing the transformedlayer into a composite image.
 29. A system comprising: at least oneprocessor, and a storage configured to store instructions, theinstructions, when executed by the at least one processor, causing thesystem to effectuate a method comprising: acquiring a target image, thetarget image including a plurality of elements, an element correspondingto a pixel or a voxel; decomposing the target image into at least onelayer, the at least one layer including a low frequency sub-image and ahigh frequency sub-image; transforming the at least one layer; andreconstructing the transformed layer into a composite image. 30.(canceled)